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DIVERSIFICATION, DERIVATIVE USAGE, AND FIRM VALUE 






By 
LARRY A. FAUVER 



A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL 

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT 

OF THE REQUIREMENTS FOR THE DEGREE OF 

DOCTOR OF PHILOSOPHY 

UNIVERSITY OF FLORIDA 

2000 



r 



Copyright 2000 

by 
Larry A. Fauver 






To my wife (Deborah), and my parents (Larry and Marcella) 






ACKNOWLEDGMENTS 
I am grateful to my chairman, Joel Houston, Mark Flannery, and P.J. van Blokland for 
their helpful comments and suggestions. I am especially grateful to my co-chair, Andy Naranjo, 
for his belief in my ability and continued patience and guidance in this work. I would also like to 
thank June Nogle, who accepted question after question when I was learning SAS. I thank my 
wife, Deborah, for her encouragement and motivation throughout this process. She has been very 
patient and supportive, and I am fortunate to call her my wife. Finally, I would like to thank my 
parents, Larry and Marcella. It is a credit to them that I am here at this point in my life. They 
have given me so many things throughout the years. I express a sincere thank you to them. 



IV 



TABLE OF CONTENTS 

page 

ACKNOWLEDGMENTS iv 

ABSTRACT vi 

CHAPTERS 

1 INTRODUCTION 1 

2 FIRM VALUE AND CAPITAL MARKET DEVELOPMENT 4 

Introduction 4 

Data 12 

Summary Statistics 13 

Methodology 14 

Results 17 

Conclusion 32 

3 FIRM VALUE AND INTERNATIONAL DIVERSIFCATION 54 

Introduction 54 

Data and Methodology 57 

Results 61 

Conclusion 72 

4 FIRM VALUE AND DERIVATIVE USAGE 93 

Introduction 93 

Data and Methodology 96 

Results 98 

Conclusion 105 

5 DISCUSSION AND CONCLUSION 116 

REFERENCES 117 

BIOGRAPHICAL SKETCH 123 



Abstract of Dissertation Presented to the Graduate School 

of the University of Florida in Partial Fulfillment of the 

Requirements for the Degree of Doctor of Philosophy 

DIVERSIFICATION, DERIVATIVE USAGE, AND FIRM VALUE 



By 

Larry A. Fauver 

August 2000 
Chairman: Joel F. Houston 
Major Department: Finance, Insurance, and Real Estate 

This dissertation investigates the connection between firm diversification, derivative 
usage and their effects on firm excess value. First, we calculate the value of corporate 
diversification on the level of capital market development. With a sample of more than 8,000 
firms from 35 countries during 1991-1995, we find that the value of corporate diversification is 
negatively related to the level of capital market development. We also find that the value of 
corporate diversification varies with the legal system. 

Second, we provide evidence on the value of product and geographic diversification and 
their impact on firm excess value. We collect data on more than 4,000 firms from four countries 
(Germany, Japan, the U.K., and the U.S.), from 1991-1995, and find that U.S. and Japanese 
multinationals trade at a higher value relative to a domestic firm operating with the same product 
mix. However, we find that multinationals do no better than a comparable international portfolio 
of firms in their respective domestic market. 

Finally, we evaluate the effects of derivative usage on firm excess value as well as the 
interactions among derivative usage, product diversification, geographic diversification, and firm 
excess value. With a sample of more than 1,600 firms from the U.S., from 1991-1995, we find a 



vi 



value loss for industrially diversified firms that use derivatives, with the greatest loss occurring 
for large diversified firms. Results that differentiate between expected and unexpected derivative 
usage and control for individual firm characteristics suggest that the value loss is associated with 
unexpected derivative usage by industrially diversified firms. 



vii 



CHAPTER 1 
INTRODUCTION 

The issue of corporate diversification and firm value has generated substantial interest in 
recent years. Potential benefits may include operating synergies, and the ability to effectively 
allocate scarce capital through an internal capital market. Costs may include increased agency 
conflicts, which may lead to increased costs. The empirical evidence, however, appears to 
indicate that firms that industrially diversify, at least in the U.S., perform worse relative to firms 
that are concentrated in one line of business. The main reason attributed to the empirical findings 
is that diversified firms may encounter greater agency conflicts and consequently more costs than 
a firm with a focused organizational structure. The connection between corporate diversification 
and firm value is less obvious outside the U.S. because it is unclear that these potential agency 
costs are similar across countries and legal systems. 

There is also the issue of product and geographic diversification and their effect on firm 
value. International diversification may allow firms to capitalize on operating and financial 
synergies, and capture possible growth opportunities. The potential benefits may also include 
portfolio diversification for shareholders. However, this benefit may not accrue to the 
multinational itself. The potential costs may include additional costs from managing more 
resources, increased political and exchange rate risks, and foreign government intervention. The 
empirical evidence is mixed and inconclusive. Therefore, the effect of international 
diversification on firm value is uncertain. 

The third issue is derivative usage, diversification, and their effect on firm value. 
Derivative instruments may allow firms to control for the costs and the risks associated with 
diversification. The theoretical evidence determines firms may use derivative instruments for tax 
motives, reduction in bankruptcy costs, and leverage, asymmetric information and moral hazard 

1 



stories among others. The empirical evidence mainly supports the theoretical evidence for 
derivative usage by firms. Evidence regarding the direct effect of derivative usage and firm value 
is not as comprehensive. This can be attributed to the lack of detailed historical reporting of 
derivative instruments by firms. The potential benefits of derivative usage may include cash flow 
smoothing, increased debt capacity, and risk transfer, among others. Potential costs may include 
increased agency conflicts when using derivatives, and increased risks. Some empirical evidence 
indicates an increase in firm value when using derivative instruments. Although it appears that 
more research is needed before resolving that derivative usage is positively related to firm value. 

This dissertation explores the connection between firm excess value, diversification, and 
derivative usage. First, we calculate the value of product diversification on the level of capital 
market development. We examine a sample of more than 8,000 firms from 35 countries during 
1991-1995. We observe that the value of corporate diversification is inversely related to the level 
of capital market development. We also uncover that the value of product diversification vanes 
with the legal system. These results suggest that the financial, legal, and regulatory environment 
all have a significant influence on the value of product diversification, and that the optimal 
organizational structure for firms operating in emerging markets may be unrelated to those firms 
operating in more developed economies. 

Second, we provide evidence on the value of product and geographic diversification and 
its impact on firm excess value. The sample consists of more than 4,000 firms from four 
countries (Germany, Japan, the U.K., and the U.S.), from 1991-1995. Our regression analysis 
controls for the firm's size, profitability, capital intensity, and ownership structure. We find that 
the U.S. and Japanese multinationals outperform the domestic firms operating within the same 
line of business. However, we find that multinationals in each country do not outperform an 
international portfolio of firms in each of their domestic markets. These results are consistent 
with the international investments literature which suggest that shareholders can earn an 



equivalent risk-adjusted return from a multinational by holding a portfolio of domestic firms in 
each international market. 

Finally, we evaluate the effects of derivative usage on firm exces's value as well as the 
interactions among derivative usage, product diversification, geographic diversification, and firm 
excess value. With a sample of more than 1,600 firms from the U.S., from 1991-1995, we find 
that focused firms that use derivative instruments have higher unconditional average excess 
values than diversified firms that do not use them. After using regression procedures that control 
for firm characteristics including firm profitability, growth opportunities, size, leverage, dividend 
structure, and ownership concentration, we also find that the value loss is larger for industrially 
diversified firms that use derivatives, with the greatest loss occurring for large diversified firms. 
These results are consistent with greater agency costs in large, diversified firms. Interestingly, 
results that differentiate between expected and unexpected derivative usage and control for 
individual firm characteristics suggest that the value loss is associated with unexpected derivative 
usage by industrially diversified firms. These findings also suggest that when firms use 
derivatives as expected, there are no valuation effects. 



CHAPTER 2 
FIRM VALUE AND CAPITAL MARKET DEVELOPMENT 

Introduction 

The connection between corporate diversification and firm value continues to generate 
substantial interest among financial theorists and practitioners. Recent evidence suggests that 
diversified U.S. firms trade at discounts compared to firms that are more focused [e.g., Lang and 
Stulz (1994), Berger and Ofek (1995), John and Ofek (1995), and Comment and Jarrell (1995)].' 
One explanation for these findings is that diversified firms face higher agency costs as a 
consequence of their organizational form. For example, recent papers have argued that intra-firm 
coordination problems are likely to be more extensive for diversified firms, because of their need 
to allocate capital among their various disparate activities [e.g., Rajan and Zingales (1996b) 
Scharfstein and Stein (1997), and Scharfstein (1998)]. 2 

Despite the observed costs arising from corporate diversification, there is theoretical 
work that suggests that there may also be benefits from diversification. In particular, work by 
Williamson (1975), Gertner, Scharfstein and Stein (1994), Harris and Raviv (1996), and Stein 
(1997) suggests that capital constrained firms may establish internal capital markets that are able 
to effectively allocate scarce capital within the firm. 3 Recent empirical evidence documents that 
there are systematic patterns in the internal allocation of capital in diversified firms [e.g., Shin 



These results are also consistent with the evidence that corporate spin-offs generally enhance 
shareholder value [see, for example, Hite and Owers (1983), Schipper and Smith (1983), and 
Kaplan and Weisbach (1992)]. 

" Denis, Denis and Sarin (1997) argue that value-reducing diversification strategies are sustained 
over time because they benefit managers (at the expense of shareholders), but that a competitive 
corporate control market may spur many firms to increase their focus. 

It is interesting to note, however, that Stein's model actually implies that internal capital 
markets may work best among firms that are more focussed. 



and Stulz (1997), Lamont (1997), Houston, James and Marcus (1997), and Scharfstein (1998)], 
but it remains an open question whether this allocation works to increase or decrease shareholder 
value. 

It also remains an open question whether or not the extant empirical evidence extends 
beyond the results reported for U.S. firms. On one level, the agency costs accompanying 
diversification may vary systematically across countries and legal systems. At the same time, 
Khanna and Palepu (1997) argue that the relative costs and benefits of corporate diversification 
depend critically on the "institutional context" in which the firm operates. The institutional 
context includes the financial, legal, and regulatory environment. In a similar vein, LaPorta, 
Lopez- De-Silanes, Shleifer, and Vishny (1997) show that different legal systems provide 
investors with varying degrees of protection which, in turn, affect the level of economic and 
capital market development. 4 These results also suggest that the value of corporate 
diversification is related to the legal system. While diversification may have limited value in a 
developed economy such as the U.S. where the institutional context enables smaller, stand-alone 
firms to raise capital, it may be more valuable for firms who find it costly or impossible to raise 
external capital, either because of imperfect information or incomplete capital markets. 5 

A firm's access to external capital depends on its ability to obtain domestic and/or foreign 
capital. Consequently, the extent to which capital markets are developed within the country 
where the firm operates, and the extent to which that country is able and/or willing to attract 
foreign capital, will both have a strong influence on a firm's ability to raise capital. We would 
expect that internal capital markets are most valuable among firms and economies where it is 



4 Demirguc-Kunt, and Maksimovic (1996) also find that legal systems affect growth rates and the 
ability to enter into long-term financial contracts. Desai (1997), moreover, finds that 
multinational firms employ internal capital markets to overcome the higher costs of external 
finance associated with weaker creditor rights in lesser developed markets. 

The economic and legal environment in less developed markets may also make it more difficult 
to contract with other firms, and therefore, may provide an additional benefit to diversification. 
Another potentially important benefit from diversification is the relatively high level of political 
influence that conglomerates and business groups wield in less developed markets. These 
political connections can create differential access to resources and markets. 



costly to obtain external capital. Therefore, unless the agency costs accompanying diversification 
are significantly higher in these countries, we would expect that the benefits from diversification 
would be higher in countries where capital markets are less developed and where legal systems 
provide limited protection to investors. If this conjecture is correct, it raises the possibility that 
the results indicating a diversification discount for the U.S. do not generalize to other countries 
where external capital markets are less developed. 6 In particular, we would expect to see smaller 
diversification discounts, and perhaps even diversification premiums, among firms that operate in 
less developed markets. 

To date, the international evidence regarding corporate diversification has been limited. 
One notable exception is the recent work by Lins and Servaes (1999). Looking at a sample of 
firms from Germany, Japan, and the United Kingdom in 1992 and 1994, they report valuation 
discounts that are of similar magnitude to those reported for U.S. firms. Moreover, their 
estimated diversification discounts remain statistically significant for Japan and the United 
Kingdom even after controlling for firm characteristics. In Germany, after controlling for firm 
characteristics, they also report a diversification discount, but it is not statistically different from 



zero. 7 



Also notable is the recent work by Khanna and Palepu (1997). They argue that 
diversification may be more valuable in emerging markets than in more developed economies. 
Khanna and Palepu's analysis focuses on diversified business groups within India. They find that 
larger diversified groups that are in a better position to tap external capital outperform smaller 



This argument might also suggest that the value of diversification within a given country may 
decline over time as the country's capital markets become more developed. Servaes (1996), 
Klein (1998), and Hubbard and Palia (1998) have examined this issue by considering the value of 
diversification in the U.S. during the conglomerate wave of the 1960s. 

In a more recent paper, Lins and Servaes (1998) use data from 1995 to investigate the value of 
corporate diversification for Hong Kong, India, Indonesia, Malaysia, Singapore, South Korea, 
and Thailand. They find that for six of their seven countries, there is no statistically significant 
diversification discount -- only for South Korea did they find a diversification discount that was 
statistically different from zero. 



unaffiliated firms. Khanna and Palepu's study provides some indirect support for our hypothesis 
that the value of diversification depends critically on the level of capital market development. 

In this paper, we investigate the link between capital market development and the value 
of corporate diversification. To address this issue more extensively, we have assembled a large 
data set that consists of more than 8,000 firms from 35 countries over a five-year period between 
1991 and 1995. Using the methodology employed by Berger and Ofek (1995) and Lins and 
Servaes (1999), we calculate the implied value gain or loss from diversification. In addition, we 
test whether the gain or loss that results from diversification is systematically related to the level 
of capital market development. 

Our results provide evidence that the value of diversification is related to the degree of 
capital market development. In particular, after controlling for the legal environment in which 
the firm operates and firm-specific factors such as firm size, capital structure, profitability, and 
ownership structure, we find that the value of diversification varies with the degree of capital 
market development. Among high-income countries, where capital markets are well developed, 
we find a statistically significant diversification discount. This finding is consistent with the U.S. 
evidence and the international evidence presented by Lins and Servaes (1999). By contrast, for 
the lower income countries, we find that there is either a significant diversification premium or no 
diversification discount. For these firms, the benefits of diversification appear to offset the 
agency costs of diversification. These results are consistent with Khanna and Palepu's evidence 
from Indian business groups. 

We also find that the diversification discount systematically varies with the legal system. 
LaPorta, Lopez-De-Silanes, Shleifer, and Vishny (LLSV, 1997) document that the English legal 
system provides the most protection to capital providers. If this protection results in better access 
to external capital, the benefits of internal capital markets and corporate diversification may 
arguably be smaller in countries that operate under a legal system with English origin. Consistent 
with this argument, we find that diversification discounts are largest among countries where the 



8 

legal system is of English origin. We find smaller diversification discounts in countries where the 
legal system is of a German, Scandinavian, or French origin. 

Lastly, we find that our results are robust with respect to controlling for the agency costs 
associated with concentrated ownership, differences in accounting rules across countries, and 
various measures of capital market development and the legal environment. 

The rest of the paper proceeds as follows. The next section reviews the connection 
between capital market development, economic development, and legal systems. We also 
describe the various economic development classifications and legal systems for each of the 35 
countries in our sample. The third section describes our data and the methodology used to 
calculate the value of corporate diversification. The cross-country mean estimates of the value of 
corporate diversification are presented in the fourth section. Regression results regarding the 
value of diversification after controlling for firm-specific characteristics are presented in section 
five. In section five, we also provide a number of robustness tests, including the effects of 
controlling for cross-firm and cross-country differences in accounting practices. Section six 
examines the links between the value of diversification and ownership structure, while section 
seven provides a conclusion. 
Corporate Diversification and Capital Market Development 

One clear drawback of corporate diversification is that it creates another layer of potential 
agency problems within the firm. Internal politics and imperfect information within the firm may 
complicate the ability of senior managers to effectively allocate capital among the various lines of 
business that exist within a conglomerate [see, for instance, Rajan and Zingales (1996b), 
Scharfstein and Stein (1997), and Scharfstein (1998)]. Despite these costs, corporate 
diversification may still be beneficial. In some cases, combining different lines of business 
within the same organization may generate value-creating operating synergies. Diversification 
may also create financial synergies to the extent it reduces the cost of obtaining capital [see, for 



instance, Lewellen (1971), Stein (1997), Williamson (1975) and Hadlock, Ryngaert and Thomas 
(1998)]. 

The financial synergies arising from diversification are likely to vary with the level of 
capital market development. For example, Raj an and Zingales (1996a) suggest that there are 
important cross-country differences in access to capital markets. They demonstrate that the 
development of a country's financial sector reduces the cost of external finance. In 
demonstrating a link between financial development and economic growth, they show that firms 
operating in industries which are generally more reliant on external finance grow faster if they are 
established in a country that has a more developed financial system. These results are consistent 
with our main hypothesis that the value of diversification is greater in countries where capital 
markets are less developed. 

At the same time, the agency costs of diversification are also likely to vary across firms 
and across countries. While it is difficult to directly measure these agency costs, a long-standing 
literature suggests that these costs (and therefore ultimately firm value) may be correlated with 
ownership structure [see, for example, Demsetz and Lehn (1985), Morck, Shleifer and Vishny 
(1988), Holderness and Sheehan (1988), and McConnell and Servaes (1990)]. Moreover, recent 
work by La Porta, Lopez-de-Silanes, and Shleifer (1999) and by Claessens, Djankov, Fan and 
Lang (1998) indicate that ownership structure as well as the correlation between ownership 
structure and firm value, vary across countries and legal systems. While ownership concentration 
is likely to affect firm value, it remains an open question whether it also has an effect on the value 
of corporate diversification. In Section VI, we address the agency costs of diversification by 
explicitly controlling for ownership concentration among the subset of firms where these data are 
available. 

Another factor that may attenuate the link between the value of corporate diversification 
and the degree of capital market development is the increased integration of global capital 
markets in recent years. Indeed, if capital markets are perfectly integrated, we would expect that 



10 

firms would be able to access external capital at the global cost of capital, even if the financial 
sector is less developed in the country in which they operate. Empirical studies on the degree of 
capital market integration performed for various markets and under varying assumptions have 
yielded mixed results [see, for instance, Jorion and Schwartz (1986), Cho, Eun and Senbet 
(1986), Wheatley (1988), Gultekin, Gultekin and Penati (1989), Mittoo (1992), Chen and Knez 
(1995), Bekaert and Harvey (1995), Naranjo and Protopapadakis (1997), and Stulz (1999)]. 
Given these mixed results, the link between capital market development and the value of 
corporate diversification is ultimately an empirical question. 

In order to test our main hypothesis, we need to measure capital market development 
across countries. Capital market development can be measured in a variety of ways including 
per-capita GNP, equity market capitalization relative to GNP, the number and dollar amount of 
per-capita initial public offerings, the ratio of public and private debt to GNP, and the relative size 
of the banking system. 8 In our analysis, we rely on recent research which demonstrates that there 
is a strong link between capital market development and economic development [see, for 
example, Levine (1997), King and Levine (1993a, 1993b) and Rajan and Zingales (1996a)]. 
While the causation may be unclear, countries with higher levels of economic development (on 
the basis of traditional measures such as per-capita GNP) are likely to have a more extensive 
domestic capital markets and are also more likely or willing to obtain foreign capital. 9 

We primarily use two proxies to test whether capital market development influences the 
value of corporate diversification. First, relying on the link between capital market development 



8 King and Levine (1993a), Rajan and Zingales (1996a), and LaPorta, Lopez-De-Silanes, Shleifer, 
and Vishny (1997) provide a more detailed discussion of these capital market development 
measures. The problem with many of these measures is a lack of comprehensive data. 
Furthermore, some of these other measures may provide a misleading depiction of the 
accessibility of external capital. For example, measures of equity market capitalization relative to 
GNP are typically low for many European countries, but most would argue that European firms 
have good access to external financial markets. 

One potential problem with using per-capita GNP as a measure of capital market development is 
that some countries with vast natural resources may demonstrate high per-capita GNP, even 
though firms that operate in these markets have limited accessibility to external capital. None of 
the countries in our sample, however, fall into this category. 



11 

and economic development, we use the World Bank's classification of economic development as 
a proxy for capital market development. Each year, the World Bank classifies countries into four 
categories: high income, upper-middle income, lower-middle income, and low income. This 
classification is largely based on the country's per-capita GNP. With this in mind, we also 
employ the country's per-capita GNP itself as a proxy for capital market development. 10 

In addition to these proxies, we also control for the country's legal system to take into 
account the evidence by LaPorta, Lopez-De-Silanes, Shleifer, and Vishny (1997, 1998), which 
documents a link between legal systems and capital market development. LLSV classify 
countries into four different legal systems: those with English, French, German, and Scandinavian 
origin. Their evidence suggests that a country's legal system significantly affects the level of 
protection that is given to investors, which in turn affects the availability of external capital. In 
particular, they find that the English system, with its common law origin, provides investors with 
the strongest legal protection, while the French legal system provides the least protection. They 
also argue that countries whose legal system is of German or Scandinavian origin have a 
moderate level of investor protection, falling somewhere between the English and French 
systems. Controlling for economic development, we would therefore expect that diversification 
discounts would be largest among countries with an English legal system, since firms in these 
countries are likely to have better access to external capital markets. 

Table 2-1 summarizes the economic development and legal system classifications for 
each of the 35 countries in our sample. We use the legal classifications reported in LLSV. The 
average per-capita GNP is the five-year arithmetic average over our sample period, 1991-1995. 



As additional measures of capital market development, we also use for each country the ratio of 
the stock market capitalization held by minorities plus the sum of bank debt of the private sector 
and outstanding non-financial bonds to GNP (MKTCAP + Debt/GNP), the ratio of the number of 
domestic firms listed in a given country to its population (Domestic Firms/Pop), and the ratio of 
the number of the initial public offerings of equity in a given country to its population 
(IPOs/Pop). These data are obtained from LaPorta, Lopez-De-Silanes, Shleifer, and Vishny 
(1997). See Section V, sub-section C. 

From LLSV, we also obtain the law and order tradition (Rule of Law) in each country. See 
Section V, sub-section C. 



12 

This measure ranges from $316 in India to $36,800 in Switzerland. As indicated above, the 
World Bank classification largely coincides with per-capita GNP. 

Data 
Sample Construction 

Our main data source is the Worldscope database. 12 Worldscope has complete financial 
data and business segment data for more than 8,000 companies, located in 49 countries. The 
firms in the databank represent 86 percent of global market capitalization. The business segment 
data start in 1991. For this reason, our sample period begins in 1991 and extends through 1995.' 3 
We use the reported business segment data to classify the publicly traded firms as either single- 
segment (focused) or multi-segment (diversified). We classify firms as single-segment firms if 
they operate in only one two-digit SIC code industry. Firms are classified as multi-segment if 
they have more than one reported segment, and the largest segment has less than 90 percent of the 
total sales for the company. 

Within each country, we exclude multi-segment firms from the sample if the company 
does not report sales at the individual segment level. However, in cases where individual 
segment sales are not reported and there is only one primary reported SIC, we classify the firm as 
a single-segment firm and use the firm's total sales. 14 We also exclude firms whose primary 
business is financial services (i.e., where more than fifty percent of firm sales come from SICs in 
the 6000-6999 range). These firms are excluded because sales figures are irregularly reported 
and are difficult to interpret for financial institutions. Finally, we exclude firms where there are 
no pure play matches and corresponding segment sales exceed 25 percent of total sales. For 14 of 



12 This databank is also used by LaPorta, Lopez-De-Silanes, Shleifer, and Vishny (1997, 1998), 
Lins and Servaes (1999), Claessens, Djankov, Fan and Lang (1998), and LaPorta, Lopez-De- 
Silanes and Shleifer (1999). 

We wish to thank Worldscope for providing us with machine-readable access to their databank. 

Due to data limitations, we are unable to disentangle firms that may be diversified, but only 
report one line of business. 



13 

the 49 countries, there were insufficient data to calculate the estimated value of diversification, 
leaving 35 countries with sufficient data.' 5 ' 6 

Summary Statistics 

Table 2-2 reports firm level summary statistics broken down by the level of economic 
development and the legal system in which the firm is headquartered. Panel A divides the firms 
according to their country's World Bank classification. Across the four classifications, 
diversified firms have a mean number of segments varying from just over 2.5 segments in the 
high-income countries to just under 3 segments in the upper-middle income countries. In 
virtually all cases, diversified firms are significantly larger than the focused firms in terms of both 
total assets and total capital. We also find that there is no consistent distinction in the leverage 
ratios between the single and multi-segment firms in our sample. 17 

Looking at the firm level characteristics for the high-income country group, we find that 
single-segment firms have a higher average market-to-sales ratio. This evidence is consistent 
with the results of Lang and Stulz (1994), Berger and Ofek (1995), and Lins and Servaes (1999), 
and provides broad evidence suggesting that single-segment firms are valued more highly than 



15 We also exclude firms where the actual value (imputed value) is more than four (one- fourth) 
times the imputed value (actual value) - see Section IV, sub-section A. Firms are primarily 
excluded from our sample according to the following two screens: firms whose primary business 
is financial services and firms where the actual value (imputed value) is more than four (one 
fourth) times the imputed value (actual value). These two screens account for 87 percent of the 
firms eliminated from our sample, while only 2 percent of the firms are excluded from our sample 
due to multi-segment firms that do not report sales at the individual segment level. 

16 Our sampling procedure differs from Lins and Servaes' (1999) in three ways. First, they 
exclude service firms - the reason being that were relatively few service firms in Germany, and 
they wanted to control for industry differences across the three countries that they were 
investigating. In our study, we have chosen to include the broadest possible sample of firms and 
countries. Second, Lins and Servaes also exclude firms that do not trade on the country's main 
exchange. Third, to keep the data collection process manageable, Lins and Servaes only use a 
random sample of 450 firms from Japan and the United Kingdom in 1992 and 1994, whereas we 
use all firms in the databank that meet our screens. While our sampling procedure is somewhat 
different, the estimated diversification discounts that we find for Japan, Germany, and the United 
Kingdom are quite similar to those reported by Lins and Servaes (1999). 

Lins and Servaes (1999) also find no distinction in leverage ratios between focused and 
diversified firms, while Lewellen (1971), Kim and McConnell (1977), Comment and Jarrell 
(1995), and Berger and Ofek (1995) find that diversified U.S. firms have higher debt ratios. 



14 

diversified firms. However, this result does not generalize to the lesser-developed countries. In 
two of the other three classifications (upper-middle and low-income), the diversified firms have a 
median market-to-sales ratio that is higher than that found for the focused firms. For the other 
two ratios, operating income-to-sales and capital expenditure-to-sales, there is no significant 
distinction between the single and multi-segment firms. 

In Panel B, the firms are divided according to the legal system of the country in which 
they are headquartered. Once again, the results indicate that diversified firms are generally 
larger, although this difference does not appear to be significant for countries with a French legal 
system. Consistent with the results reported earlier, diversified firms generally have a lower 
market-to-sales ratio, which again provides indirect evidence that diversified firms trade at a 
discount relative to focused firms. We address this issue more completely in the next section 
where we directly estimate the value of diversification. 

Methodology 
Estimating the Value of Corporate Diversification 

To estimate the value of corporate diversification, we modify the approach originally 
used by Berger and Ofek (1995). In our analysis, we use the ratio of total-capital-to-sales to 
measure corporate performance, where total capital is calculated by adding the market value of 
equity to the book value of debt. Along with this measure, Berger and Ofek (1995) also consider 
two other ratios to measure performance: the ratio of total-capital-to-assets and the ratio of total- 
capital-to-earnings. Their qualitative results are similar for each of the three performance 
measures. We are unable to use these alternative measures because there is very little business 
segment data regarding assets or earnings for the non-U.S. firms. 18 

We calculate the excess value of each firm by taking the difference between the firm's 
actual performance and its imputed performance. Actual performance is measured by the 
consolidated firm's capital-to-sales ratio. For single-segment firms, imputed value is calculated 



For similar reasons, Lins and Servaes (1999) also use the capital-to-sales-ratio as their sole 
measure of performance. 



15 

as the median capital-to-sales ratio among all pure-play (single-segment firms) within the same 
industry and same country. For multi-segment firms, imputed value is calculated by taking a 
weighted-average of the imputed values for each of the firm's segments, where the weights 
reflect the proportion of the overall firm's sales that come from each segment. Multi-segment 
firms have a positive excess value (i.e., a premium) if the overall company's value is greater than 
the "sum of the parts." By contrast, multi-segment firms have a negative excess value if their 
value is less than the imputed value that would be obtained by taking a portfolio of pure-play 
firms that operate in the same industries and country as the diversified firm. 19 

We define industries at the two-digit SIC code level. 20 In cases where there are no other 
two-digit pure-plays firms to match from, we calculate the imputed market capital-to-sales ratio 
using broader industry classifications defined by Campbell (1996). 21 Finally, to avoid having the 
results driven by extreme values, we exclude firms where the actual value is more than four times 
the imputed value, or where the imputed value is more than four times the actual value. 22 
The Value of Diversification 

Table 2-3 reports the excess value estimates for the single and multi-segment 
firms in our sample. Once again, the firms are classified according to each country's legal system 
and the World Bank's classification of economic development for each country. 



19 The average number of pure-plays ranges from 1 .30 in New Zealand to 29.44 in the U.S., while 
the average number of pure-plays in the less developed markets is 3.02. To further insure that our 
results are robust with respect to the control groups, we also increased the required minimum 
number of pure-play matches to three firms and obtained similar results, but with a considerably 
smaller sample. 

While this two-digit classification is somewhat coarse, it provides us with a larger number of 
pure play firms. Increasing the number of pure-plays is particularly important in the less 
developed markets. Lins and Servaes (1999) and others also use a similar approach. 

The reported results are essentially the same if we eliminate firms from the sample that do not 
have a two-digit pure-play match. 

22 Berger and Ofek (1995) and Lins and Servaes (1999) also use this screen. When we use a more 
conservative screen of excluding firms where the actual value (imputed value) exceeds the 
imputed value (actual value) by a factor of three, we obtain similar results. 



16 

The results in Panel A, where the firms are divided according to the World Bank 
classification, strongly suggest that the value of diversification is negatively correlated with the 
degree of economic development. Diversified firms in the most developed nations trade at a 
significant discount relative to focused firms. The median discount for the high-income group is 
5.76 percent. By contrast, diversified firms in the low-income group trade at a significant 
premium of 3.80 percent relative to focused firms. This finding suggests that diversification may 
create net benefits among firms that operate in countries whose capital markets are not fully 
developed, which is consistent with the evidence from Indian business groups reported by 
Khanna and Palepu (1997). 

One potential concern with the World Bank classification is that there are relatively few 
firms (particularly diversified firms) within the lower income groupings, and these firms come 
from a relatively small number of countries. For example, there are only three countries in our 
sample that are in the low-income group - China, India, and Pakistan. A concern that arises is 
that it may be difficult to sort out whether any demonstrated effects for this group are due to its 
low-level development, or to other country-specific factors. While we control for these effects 
more completely in the subsequent regression analysis, another way to get at this issue is to 
broaden the categories of economic development. Thus, in Panel B, we report similar excess 
values, but the countries are divided more broadly according to their per-capita GNP. In this 
classification, the lowest grouping also includes firms operating in Indonesia and the Philippines. 

In Panel B, the mean and median excess values, using the broader per-capita GNP 
groupings, are very similar to statistics reported in Panel A using the World Bank classifications. 
Once again, the value of diversification varies with the level of economic development. Firms 
that operate in countries with a per-capita GNP in excess of $15,000 have a mean diversification 
discount of 5.79 percent and a median discount of 5.78 percent. The results are also strikingly 
different for firms headquartered in the emerging market countries. Among these firms, we find a 
mean diversification premium of 8.41 percent and a median premium of 5.41 percent. The 



17 

similarity between Panel A and Panel B confirms that the World Bank classifications are largely 
driven by differences in per-capita GNP. 

In Panel C, we classify firms according to their country's legal system. The results 
indicate that diversified firms trade at substantial discounts if they operate in a country with a 
legal system of English origin. Among these countries, the median discount is 8.57 percent. 
Among the other countries in our sample with French, German, and Scandinavian legal origin, we 
find no evidence of either a diversification discount or premium. These results complement the 
evidence reported by LLSV (1997). Their results suggest that the English legal system provides 
the most protection to external investors which generally leads to more developed capital 
markets. Our results suggest that the value of internal capital markets is smallest when capital 
markets are most developed. 

Results 
The results reported in Table 2-3 suggest that the degree of capital market 
development affects the value of corporate diversification. While these results provide an overall 
depiction of the value of diversification among various countries, they do not control for 
individual firm characteristics, which may also affect the firm's market-to-sales ratio. These 
other characteristics include the firm's size, profitability, and future growth opportunities. To 
control for these factors, we estimate the following regression model for each of the thirty-five 
individual countries in our sample: 23 

(1) Excess Value = a + /3 ] (Diversification Dummy) + /? 2 (Log Assets) 

+ ^(Operating Income/ Sales) + j3 A (Capital Expenditures I Sales) + e. 

Excess value is defined to be the natural log of the ratio of the firm's market value to its 

imputed value. The diversification dummy (SEG) is equal to one for multi-segment firms and is 

otherwise zero. The log of assets controls for potential firm size effects. The ratio of operating 

income-to-sales (OIS) provides a measure of firm profitability, while the ratio of capital 



23 Lang and Stulz (1994), Berger and Ofek (1995), and Lins and Servaes (1999) also estimate 
similar models. 



18 

expenditures-to-sales (CES) proxies for the level of growth opportunities. Controlling for the 
other factors, we would expect to see a positive link between excess value and both OIS and CES. 
Since our data cover five years (1991-1995), we also include separate year dummies in the 
regressions to control for intertemporal variations in market or economic conditions that may also 
affect the firm's market-to-sales ratio. 
Regression Results for the Individual Countries 

The regression results for the individual countries are reported in Table 2-4. In 23 of the 
35 countries, the estimated coefficient on the diversification dummy variable is negative. In 1 1 of 
these 23 countries, the coefficient is statistically significant, suggesting a diversification discount. 
In 12 of the 35 countries the coefficient is positive. In 4 of the 12 cases (Hong Kong, Norway, 
Pakistan and Singapore), this coefficient is positive and statistically significant, suggesting that 
there is a diversification premium for these countries, after controlling for the firm-level 
characteristics. 

As expected, we find that the estimated coefficients on the OIS (Operating Income/Sales) 
and CES (Capital Expenditures/Sales) variables are generally positive and frequently significant. 
These results confirm that firms that are more profitable and that have greater growth 
opportunities typically have higher market-to-sales ratios. The signs on the estimated coefficients 
for the log of asset variable vary considerably across the different countries. The previous 
evidence on this variable is also mixed - Berger and Ofek (1995) find a positive link between 
firm size and firm value, while Lang and Stulz (1994) and Lins and Servaes (1999) find a 
negative relation. Although not reported, the annual dummy coefficients indicate that there is 
little time variation in the excess values, after controlling for firm characteristics. 

The estimated coefficients on the diversification dummy appear to be reasonable and are 
generally well within the ranges found in earlier studies. Among U.S. firms, we find a 
diversification discount of 13.2 percent, which is similar to the 14.4 percent found by Berger and 
Ofek (1995) over an earlier time period 1986-1991 . For Germany, we find no evidence of a 



19 

statistically significant diversification discount or premium, confirming the conclusions reached 
by Lins and Servaes (1999). Lins and Servaes also found a diversification discount for Japan of 
roughly 10 percent for both 1992 and 1994. Looking at a broader set of firms, we find a 
statistically significant diversification discount for Japan of 4 percent, which is smaller than their 
estimate. 24 Likewise, for the United Kingdom, Lins and Servaes (1999) found a 15 percent 
discount. Looking at a significantly larger sample, we also find a discount for the United 
Kingdom. Our estimated discount of 7 percent is smaller, but it remains highly significant. 

As indicated above, most of the other diversification coefficients appear to be of a similar 
magnitude to those reported for the United States, Japan, Germany and the United Kingdom. 
However, the point estimates for a couple of countries do stand out. For example, the 
diversification discount in Turkey is relatively large and marginally statistically significant, while 
in Spain the discount is both relatively large and significant at the 1 percent level. At the other 
extreme, we find a large diversification premium in both Pakistan and the Philippines, although 
the premium for the Philippines is not statistically different from zero. While in each of these 
cases the magnitude of the estimates appears to be large, the existence of a diversification 
discount or premium is generally consistent with our predictions. 

When we pool the firms in our sample along two dimensions related to the capital market 
development of the country in which the firms are headquartered (the World Bank's classification 
of development and the country's legal system), we find that there is a significant diversification 
discount of 8.2 percent among the high-income countries. 25 Interestingly, however, there is no 
evidence of a significant diversification discount or premium for the firms that are not 
headquartered in a high-income country. For these firms, it appears that the benefits of 



M 



Lins and Servaes' (1999) estimates for Japan did not include CES because many Japanese firms 
did not report CES. We obtain results that are more similar to Lins and Servaes when we also 
eliminate the CES criterion. 



is 



This result is consistent with the findings of Berger and Ofek (1995), Lang and Stulz (1994), 
and Lins and Servaes (1999), and also reaffirms the summary statistics reported in Table 2-3. 
This coefficient is highly significant with a t-statistic of -10.726. 



20 

diversification (operating synergies and the establishment of internal capital markets) roughly 
offset the costs of diversification. These findings suggest that while corporate focus generally 
makes sense in highly developed countries, its value may not extend worldwide in cases where 
external capital markets are less developed. In this regard, our results lend support to the 
conclusions reached by Khanna and Palepu (1997). 

From the pooled legal system results (also not reported), we find that there is a strong 
relation between the legal system and the value of corporate diversification. In particular, the 
observed relations are consistent with our priors and are also consistent with the evidence found 
by LLSV (1997, 1998). We find that diversification significantly reduces value in countries that 
have a legal system with English, French, or Scandinavian origin. As expected, the value of 
diversification is most negative for firms that operate in countries with an English legal system. 
Finally, controlling for OIS, CES, size, and annual variations, we find neither a diversification 
discount nor premium among the firms that operate in markets with a German legal system. 
Firm-level Regression Results 

To further test the link between capital market development and the value of 
diversification, we also estimate firm-level regressions that include all of the firms from each 
country and for each year of our sample period. In each case, the dependent variable is the firm's 
excess value. These regressions, reported in Table 2-5, control for the firm-level characteristics 
outlined above (OIS, CES, and firm size). The regressions also include variables reflecting (1) 
the level of economic development of the country in which the firm is headquartered as measured 
by the country's World Bank classification or per-capita GNP; (2) the country's legal system; (3) 
year dummies to take into account time variation in the value of diversification. 

The OLS regression estimates reported in columns (1) - (3) of Table 2-5 and the fixed- 
effects estimates reported in column (4) are for the full sample of firms (single-segment and 
multi-segment firms), where the dummy variable, SEG, equals 1 if the firm has multiple 
segments and equals otherwise. The coefficient on SEG, therefore, indicates the value of 



21 

diversification after controlling for the firm-specific, time-specific, and country-specific factors. 
The regression specification reported in the first column only controls for the firm-specific and 
time-specific factors. This specification is the same one estimated for the country-level 
regressions reported in Table 2-4. The second specification, reported in column (2), also includes 
dummy variables corresponding to the World Bank classification of economic development and 
the legal system of the country in which the firm is headquartered. In column (3), the regression 
specification includes the legal system dummy variables and the level of the country's per-capita 
GNP as a continuous variable alternative to the discrete World Bank classification dummy 
variables. Column (4) provides fixed-effects estimates of the third specification. 

The results indicate that across all firms, diversification has a negative impact on firm 
value. 26 In column (1), the estimated diversification discount for the full sample of firms is 7.8 
percent. When we control for economic development and the legal system with dummy 
variables, in column (2), the diversification discount for high-income countries with an English 
legal system is 9.6 percent. Looking at the estimates in column (2), we also see that excess value 
is significantly higher (at the 5 percent level) if the firm is from a country that is classified as low- 
income by the World Bank (Gl *SEG). In column (3), we also see that the value of 
diversification is negatively related to per-capita GNP, in that there is a statistically significant 
negative relation (at the 1 percent level) between excess value and the variable which interacts 
per-capita GNP with the diversification dummy, SEG. In terms of economic significance, the 
estimated per-capita GNP coefficient in column (3) implies a discount for the U.S. of 10.5 percent 
(-0.426 x 10" 5 x 24,758). 27 



In each case, the adjusted R 2 's are somewhat lower than those of the individual country 
estimates in Table 2-4. While there are clear benefits to pooling the countries, there is also more 
noise introduced. 

As an additional robustness check, we also estimated the regression models corresponding to 
columns (1) - (4) using only the multi-segment firms. For the multi-segment firm regressions, 
we included as our measure of diversification the number of segments, SEGN, as an additional 
explanatory variable in place of SEG. Similarly, in these regressions, each of the interacted 
variables was interacted with the number of segments (as opposed to interacting with SEG). The 
results were very similar to those reported for the entire sample. In particular, in all cases there 



22 



The legal system dummies are also significantly different from zero, and the estimated 
coefficients have the predicted signs. In particular, we find that the estimated coefficients are 
positive for the French, German, and Scandinavian legal dummy variables, indicating that 
diversification provides greater benefits and/or fewer costs relative to firms that operate in a 
country with a legal system of English origin. Looking more closely at the estimated coefficient 
for the legal system dummy variables, we also see that the coefficient for the German legal 
system is the most positive. This result suggests that after controlling for the other relevant 
factors, the net costs of diversification are the smallest for firms that operate under the German 
legal system. 

As a robustness check, we also estimate the third specification using fixed-effects. 28 
These results are reported in column (4). Similar to the OLS estimates, we find that for the 
diversified firms there is a statistically significant negative link (at the 1 percent level) between 
per-capita GNP and excess value. We also find that the German legal system provides the 
smallest diversification costs. As a further robustness check, we also estimated columns (l)-(3) 
on a year-by-year basis. Once again, the estimates (not reported) confirm the negative link 
between per-capita GNP and excess value and the variation of excess value across legal 
systems. 



was a significant negative correlation between excess value and the number of segments. We 
also found that the value of diversification was negatively related to per-capita GNP and that the 
estimated coefficients interacting the diversification variable with the World Bank income group 
dummies and with the legal system dummies had the same signs, and were generally even more 
significant than the results reported for the entire sample. 



28 



The fixed-effects estimates for the first specification in column (1) are similar to those reported 
for the OLS estimates. For the second specification in column (2), several of the coefficients can 
not be estimated using fixed-effects due to singularity of the data. The singularity arises from the 
inclusion of discrete dummy variables for development and the legal system that persist over 
time. 

!9 The development and legal system results are significant in 1992-1994. In 1995, the results are 
marginally significant. In 1991, the results are largely insignificant because there are too few 
low-income country firm observations to get precise estimates. 



23 

Additional Proxies for Capital Market Development and the Legal Environment 

Up until now, we have primarily used per-capita GNP and legal origin indicator variables 
as proxies for capital market development and the legal environment. However, it is important 
that we also employ additional measures in order to insure that our results are robust. LaPorta, 
Lopez-De-Silanes, Shleifer, and Vishny (1997) analyze several measures of capital market 
development and the legal environment across 49 countries. In particular, as measures of capital 
market development for each country, they consider the ratio of the stock market capitalization 
held by minorities to GNP (External Cap/GNP), the ratio of the sum of bank debt of the private 
sector and outstanding non-financial bonds to GNP (Debt/GNP), the ratio of the number of 
domestic firms listed in a given country to its population (Domestic Firms/Pop), and the ratio of 
the number of the initial public offerings of equity in a given country to its population 
(IPOs/Pop). LLSV also find that the law and order tradition (Rule of Law) in each country is an 
important determinant of external finance. 

In our regression analysis, we also employ the capital market development and legal 
environment proxies used by LLSV. 30 These results are shown in Table 2-6. In the first column, 
we provide firm level OLS regression estimates using the additional proxies, while the second 
column provides fixed-effects estimates. Interestingly, we find that the coefficient estimates on 
per-capita GNP, external market capitalization plus debt to GNP, and domestic firms to 
population are all negative and statistically significant, whereas the coefficient on IPOs to 
population is not statistically different from zero. We also find that the coefficient estimates on 
the legal origin indicator variables remain significant, while the coefficient on the Rule of Law 
variable is not statistically different from zero. It is also interesting to note that the fixed-effects 
estimates reported in the second column are consistent with the OLS results shown in the first 
column. 



30 



Due to a lack of debt, IPO, and /or Rule of Law data, we lose Australia, China, Hong Kong, 
Pakistan, Switzerland, and Taiwan from the analysis. If we set the missing observations equal to 
zero, we obtain similar conclusions. 



24 

Due to the high correlation between per-capita GNP and Rule of Law (0.76), we 
eliminate per-capita GNP from the specification shown in the third column. In this instance, the 
Rule of Law becomes highly significant, while the remaining coefficient estimates are similar to 
those reported in the first column. The fixed-effects estimates reported in the fourth column are 
also consistent with the OLS results where per-capita GNP is eliminated from the specification. 
All in all, we find that value of corporate diversification varies with the level of capital market 
development and legal environment. 
Accounting Issues 

Throughout our analysis, we have used the market-to-sales ratio as a proxy for firm 
value. One concern is that our results may be biased by cross-country differences in the 
accounting practices that firms employ when they hold either a majority or minority stake in 
another firm. 31 

Whenever a parent company owns a majority stake in another firm, the market value of 
the consolidated firm includes the value of its ownership stake in the subsidiary. However, 
depending on the accounting practices employed, the sales of the subsidiary may or may not be 
fully included as part of the company's consolidated sales. For firms that have a controlling stake 
in another firm, there are two basic methods of preparing consolidated financial statements. 
Under the proportional method, consolidated sales include only that portion of the subsidiary's 
sales that reflects the parent's ownership percentage in the subsidiary. 32 In this case, the market- 
to-sales ratio is not biased. Alternatively, under the full consolidation method, consolidated sales 
include all of the subsidiary's sales, regardless of the parent's ownership percentage. Clearly, this 
accounting practice biases downward the market-to-sales ratio. In these circumstances, the net 
income earned by the minority shareholders is subtracted out of the consolidated firm's total 



For examples of the various accounting methods employed across countries, see International 
Accounting and Auditing Trends by the Center for International Financial Analysis & Research, 
Inc. 

When this approach is used, the remainder of the subsidiary's sales is attributed to the minority 
interest shareholders. 



25 

income in order to arrive at consolidated net income. Consequently, whenever the minority 
shareholders' share of subsidiary sales is a significant portion of consolidated sales, we would 
expect that the market-to-sales ratio would be biased downward under the full consolidation 
method. 

Another potential problem arises when a company (Company A) owns a minority interest 
in another company (Company B), but does not choose to include its proportion of Company B's 
sales on its (Company A's) income statement. 33 In these circumstances, Company A's market-to- 
sales ratio would be biased upward, since the effects of its ownership in Company B would be 
included in its market value but not in its sales. In this situation, Company A's income from 
Company B would show up as investment income from unconsolidated affiliates. Therefore, 
whenever investment income from unconsolidated affiliates is a significant portion of net income, 
the market-to-sales ratio is likely to be upward biased. 

For our purposes, these accounting biases are particularly important if the magnitude of 
the biases vary across countries and vary between focused and diversified firms. We find that for 
5 of the 35 countries (Denmark, Hong Kong, Indonesia, Italy and Malaysia), diversified firms 
have a significantly higher proportion of minority interest income as a percentage of sales. The 
market-to-sales ratios for these countries tend to be biased downward more often for diversified 
firms, which would bias us towards finding a diversification discount in these countries. For 2 of 
the 35 countries (France and Switzerland), we find that focused firms have a significantly higher 
proportion of income from unconsolidated affiliates as a percentage of sales. The market-to-sales 
ratios for these countries tend to be biased upward more often for single segment firms, which 
would also bias us towards finding a diversification discount in these countries. 



When a company owns a 20%-50% stake in another company, it may have the option to 
include its proportion of the sales on its income statements. This approach is referred to as the 
"proportional method." Alternatively, under the "equity method," the company does not include 
the sales on its income statement and instead treats it as an investment in an unconsolidated 
affiliate. The "cost method" is generally used when a company has a stake that is less than 20%. 
The ability to select a particular accounting treatment varies across countries and across 
industries. We thank the referee and Chuck McDonald for bringing these issues to our attention. 



26 

To insure that our results are not driven by these accounting biases, we eliminated from 
our sample firms where minority interest income is greater than 2% of sales and firms where 
investment income from unconsolidated affiliates is greater than 2% of sales. After eliminating 
these firms, the link between per-capita GNP and excess value is somewhat stronger and 
statistically more significant. Moreover, there still remains a strong link between the legal system 
dummies and excess value, although the dummy corresponding to the French legal system is 
marginally significant and the Scandinavian legal system dummy is no longer significant. 
Ownership and the Value of Corporate Diversification 

The results discussed in Section V suggest that corporate diversification is less 
costly/more beneficial for firms that are headquartered in countries where capital markets are less 
developed. A potential problem with this conclusion is that, so far, we have not explicitly 
controlled for agency costs associated with ownership concentration. Indeed, several studies 
suggest that firm value is correlated with ownership structure [e.g., Demsetz and Lehn (1985), 
Morck, Shleifer and Vishny (1988), Holderness and Sheehan (1988), and McConnell and Servaes 
(1990)] and that ownership structure varies across countries and legal systems [e.g., La Porta, 
Lopez-de-Silanes and Shleifer (1997, 1998), LaPorta, Lopez-De-Silanes and Shleifer (1999), and 
Claessens, Djankov, Fan and Lang (1998)]. To the extent that ownership concentration affects 
firm value, it may also affect the estimated value of corporate diversification. This concern may 
be particularly relevant if there is a strong link between ownership concentration and firm value 
and if focused and diversified firms have significantly different levels of ownership 
concentration. 

The exact link between ownership structure and firm value, however, is not entirely clear. 
On one hand, it is widely acknowledged that concentrated ownership is likely to reduce the 
conflicts that arise when there is a separation between managers and stockholders. This link 
suggests a positive relation between firm value and ownership concentration. On the other hand, 
concentrated ownership provides large investors with opportunities to exploit minority 



27 

shareholders, thereby suggesting at least for some range of values a negative relation between 
firm value and ownership concentration. In a recent study, Holdemess and Sheehan (1998) 
conclude that in the United States, legal constraints often effectively limit the actions of majority 
shareholders - but it is not clear to what extent their conclusions extend outside the U.S. Indeed, 
La Porta, Lopez-de-Silanes and Shleifer (1999) suggest that the costs of concentrated ownership 
may be particularly meaningful in less developed countries where the legal protection provided to 
minority shareholders is often quite limited. 

An additional concern is that even if ownership concentration levels are similar for both 
focused and diversified firms, ownership concentration may still be important if it has a 
differential effect on the value of focused and diversified firms. This concern is particularly 
relevant if the costs associated with ownership concentration are lower for diversified firms in 
less developed capital markets. If this scenario is correct, it raises the possibility that cross- 
country variations in the value of corporate diversification can be explained by differences in 
capital market development as well as by differences in ownership structure. For example, 
smaller diversification discounts (or premiums) in less developed countries may be due to the fact 
that diversification is more beneficial in these markets because capital markets are less developed, 
enhancing the value of internal capital markets. Alternatively, smaller diversification discounts 
(or premiums) in less developed countries may reflect the fact that ownership concentration is 
generally higher in these countries, resulting in potentially lower agency costs associated with 
corporate diversification. Clearly, these two interpretations are not necessarily mutually 
exclusive, but they do again suggest the need to control for ownership concentration when 
calculating the value of corporate diversification. 
Ownership Data 

Worldscope provides firm level ownership data that consists of reported cases where an 
individual or institution holds at least five percent of a company's common stock. Summing up 
these reported holdings across all shareholders, we obtain a measure of ownership concentration 



28 

for each firm. 34 While ownership data are available for a subset of firms in our sample, an 
important concern arises when using this data. In many cases, there is no clear distinction 
between firms where no individual or institution holds a five percent stake and firms that choose 
not to report any ownership data. This reporting bias also appears to be systematic - in that 
ownership data is reported much less regularly among firms headquartered in less developed 
countries. 35 To insure that this reporting bias does not affect the qualitative nature of our results, 
we use two different methods to classify the unreported ownership data. In the first method, we 
treat the unreported observations as missing values. Since many of these missing observations 
are likely to be for firms without significant ownership concentration, this approach creates an 
upward bias in the level of ownership concentration. In the second method, we assign a zero 
value to the unreported observations. Using this method, the reported levels of ownership 
concentration are downward biased. 

The descriptive statistics on ownership concentration are summarized in Table 2-7. The 
results in Panel A treat the unreported observations as missing values, while the results in Panel B 
treat the unreported observations as zero values. It follows that the average ownership 
concentration levels reported in Panel B are systematically lower than those reported in Panel A. 

Three major conclusions emerge from Table 2-7. First, there does appear to be an 
ownership reporting bias in the Worldscope data. For example, in the low-income countries, 
concentrated ownership is reported for only 14% of the firms, whereas this number is 65% for the 



34 



In addition to total ownership concentration, Lins and Servaes (1998) also separate ownership 
holdings into various detailed ownership categories and find their reported conclusions to be 
largely similar across the various measures of ownership concentration. 



35 



Another potentially important problem with the reported ownership data is that in some 
countries, cross-ownership holdings and ownership pyramids are fairly common. La Porta, 
Lopez-de-Silanes and Shleifer (1999) study ownership concentration structures in considerable 
detail and estimate the magnitude of cross-holdings for the twenty largest publicly traded firms in 
various countries. As they point out, "the data on corporate ownership are often difficult to 
assemble." Since following their approach for all of the firms in our sample is prohibitive, we are 
forced to rely on the numbers reported by Worldscope. In this regard, we follow the approach 
used by Lins and Servaes (1998) and Claessens, Djankov, Fan and Lang (1998). However, it is 
important to note that Worldscope provides only limited ownership data for several countries in 
our sample. 



29 

firms in the high-income countries and 78% for the firms in the upper-middle income countries. 
Second, consistent with previous papers, we do find that average ownership concentration does 
vary across countries and legal systems. Generally, we find ownership concentration is higher in 
less developed markets and in markets where the legal system tends to provide less protection to 
investors. Third, we find that while ownership concentration varies across regions and legal 
systems, within each region and legal system, unconditional ownership concentration levels are 
similar for diversified and focused firms. 37 This result tends to suggest that our earlier results on 
the effects of capital market development on the value of corporate diversification are not driven 
solely by differences in ownership concentration. Nevertheless, in order to more clearly 
disentangle the corresponding sources of any diversification discounts or premiums, we need to 
control for ownership concentration in our regression analysis. 
Regression Results Controlling for Ownership Concentration 

Similar to Morck, Shleifer and Vishny (1988) and others, we also account for the 
nonlinear relation between ownership structure and firm value by creating three separate 
ownership concentration variables: 38 

OWNOtolO = total ownership if total ownership < 0.10, 
= 0.10 if total ownership > 0.10; 



6 Putting these two conclusions together also leads us to suspect that among the 35% of firms in 
the high income category where ownership data is not reported, a relatively high percentage of 
these firms may truly have disparate ownership and that ownership data is truly missing for only a 
small subset of these firms. Alternatively, when we consider the 86% of low income firms with 
no reported ownership data, we would suspect that a higher percentage of these observations are 
truly missing. 

Statistical tests for differences in the average level of ownership concentration between the 
focused and diversified firms are not statistically significant from zero for any of the groups. 

38 

Morck, Shleifer and Vishny (MSV, 1988) use 5 percent and 25 percent as their breakpoints. 
Given that the Worldscope databank does not generally provide firm level ownership 
concentration values below 5 percent (aside from the unreported values), we use a 10 percent cut- 
off for the first breakpoint and 30 percent as the next breakpoint to be consistent with MSV's 
ownership ranges. As additional robustness checks, we also tried other breakpoints and used 
ownership concentration dummy variables for each of the breakpoints in place of the MSV 
variables. In both cases, we found that the reported conclusions were qualitatively unchanged. 



30 



OWN10to30 =0 if total ownership < 0.10, 

= total ownership minus 0.10 if 0. 10 < total ownership < 0.30, 
= 0.20 if total ownership > 0.30; 

OWNover30 =0 if total ownership < 0.30, 

= total ownership minus 0.30 if total ownership > 0.30. 

This classification suggests that the marginal impact of increased ownership 
concentration varies depending on whether ownership concentration is less than 10 percent, 
between 10 and 30 percent, and greater than 30 percent. We also interact OWN10to30 and 
OWNover30 with the dummy variable SEG, which equals one if the firm has multiple segments, 
to assess the impact of ownership concentration on the value of corporate diversification. 39 
Generally, we would expect a positive link between firm value and OWNOtolO. Within this 
range, increases in ownership concentration are likely to improve managerial incentives without 
dramatically increasing the risks of managerial entrenchment and expropriation. For ownership 
concentration levels beyond ten percent, the expected results are less clear. For these firms, the 
benefits of increased ownership may be more than offset by the costs resulting from increased 
managerial entrenchment and by the potential for the expropriation of minority shareholders. 
Consequently, the link between OWN10to30 and OWNover30 and firm value is less clear. 

The firm level regression estimates that control for ownership concentration are reported 
in Table 2-8. The first three columns [(1) - (3)] contain the results where the unreported 
observations for ownership concentration are treated as missing. In the last three columns [(4) - 
(6)], these observations are treated as zeros. The most striking conclusion that emerges from the 
results in Table 2-8 is that even after controlling for ownership concentration, there is still a 
strong link between the value of corporate diversification and both the legal system dummies and 
per-capita GNP. Moreover, the sign and magnitudes of the estimated coefficients are quite 
similar to those reported earlier in Tables 2-5 and 2-6. 



39 



Note that due to singularity, we do not include OWN0tol0*SEG in our specification. 



31 

While it is not the primary focus of our analysis, the estimated coefficients for the 
ownership concentration are still of considerable interest. The estimated coefficients vary 
somewhat depending on the treatment of the unreported ownership observations. Nevertheless, a 
few basic conclusions emerge. First, for low levels of ownership concentration, there is a positive 
link between ownership concentration and excess value, although this link is significant only for 
the cases where we treat the unreported ownership observations as zeros. Second, for ownership 
concentration levels beyond ten percent, we generally find that increases in ownership 
concentration lead to a reduction in value for both focused and diversified firms. This result 
confirms the fact that there are both costs and benefits associated with increased ownership 
concentration. 

Finally, in columns (5) and (6), we see from the coefficients on the ownership 
concentration variables that are interacted with the diversification dummy (OWN10to30*SEG 
and OWNover30*SEG), that the effects of ownership concentration are significantly different for 
focused and diversified firms. For ownership concentration levels between 10 and 30 percent, 
excess value is significantly lower for the diversified firms, suggesting that entrenchment 
problems and expropriation of minority shareholders is more of a concern for diversified firms. 
However, beyond 30 percent, excess value is significantly higher for diversified firms. It is 
notable, however, that these results do not hold up in columns (2) and (3), where the unreported 
ownership observations are treated as zeros. 

All in all, the results suggest that there is a link between ownership concentration and 
excess value, and that this link may be somewhat different for focused and diversified firms. 
However, the exact nature of these links depends critically on the specification and on the 
treatment of the unreported ownership observations. It is also important to reiterate that 
regardless of the specification, there is strong evidence that the value of corporate diversification 
varies depending on the legal system and the level of capital market development. 






32 

Conclusion 

Using a large database of more than 8,000 companies from 35 countries, we analyze the 
link between capital market development and the value of corporate diversification. We find 
evidence that the value of corporate diversification is negatively related to the level of capital 
market development. Among high-income countries where capital markets are well developed, 
we find that diversified firms trade at a significant discount relative to focused firms. This 
evidence is consistent with previous studies (Lang and Stulz (1994) and Berger and Ofek (1995)) 
that have documented a diversification discount for U.S. firms. In contrast, we find that there is 
either no diversification discount, or in some cases, a significant diversification premium, in 
countries whose capital markets are less developed. Consistent with the recent findings of 
LaPorta, Lopez-De-Silanes, Shleifer, and Vishny (1997, 1998), we also find that the value of 
diversification depends in an important way on the legal system of the country in which the firm 
is established. 

Overall, our results suggest that the financial, legal, and regulatory environment all have 
an important influence on the value of diversification, and that the optimal organizational 
structure for firms operating in emerging markets may be very different than that for firms 
operating in more developed countries. In this regard, our results provide support for the 
arguments made by Khanna and Palepu (1997), who find that diversified industry groups in India 
often outperform their stand-alone counterparts. Our results are also consistent with Lins and 
Servaes (1999) who find that diversified firms in Japan and the United Kingdom (countries that 
are considered to be developed) generally trade at discounts relative to focused firms. 

While we have argued that cross-country variations in the value of diversification vary 
with the level of capital market development, our results can be interpreted more broadly. In 
addition to providing better access to capital markets, or limiting the need to access these 
markets, diversification may provide other important benefits - particularly in countries where 
the economic and legal system are less developed. If the economic and legal environments make 



33 

it more difficult to contract with other firms, it may be more beneficial to merge related 
enterprises within the same organization than it is to have them operate on a separate, stand-alone 
basis. Diversified firms in these countries may also be better able to attract quality employees 
and better able to lobby or influence the political and regulatory process. Ultimately, each of 
these explanations may be applicable. 

Finally, while we do not address this issue directly, our results indirectly suggest that 
global capital markets are not perfectly integrated. Firms in countries that have less developed 
capital markets appear to face a higher cost of external capital. One way to mitigate these higher 
costs is to adjust the optimal organizational structure. More specifically, for these firms, the 
establishment of an internal capital market within a diversified firm may more than offset the 
costs of corporate diversification. Clearly, however, there may be other ways to address these 
distortions. For example, Lins and Servaes (1999) stress the importance of concentrated 
ownership. Other alternatives may include the establishment of private banking relationships 
and/or the establishment of the type of interconnected business groups described by Khanna and 
Palepu (1997). These issues await future research. 



Table 2-1 
Economic Development and Legal System Measures by Country: 1991 - 1995 



34 



Country 


Average 
Per-Capita GNP (US $) 


World Bank Market 
Classification 


Legal System 
Classification 


Australia 


17,808 


High Income 


English Origin 


Austria 


23,666 


High Income 


German Origin 


Brazil 


3,134 


Upper-Middle Income 


French Origin 


Canada 


20,098 


High Income 


English Origin 


Chile 


3,206 


Upper-Middle Income" 


French Origin 


China 


498 


Low Income 


Other 


Denmark 


26,936 


High Income 


Scandinavian Origin 


Finland 


21,090 


High Income 


Scandinavian Origin 


France 


22,808 


High Income 


French Origin 


Germany 


24,188 


High Income 


German Origin 


Hong Kong 


18,588 


High Income 


English Origin 


India 


316 


Low Income 


English Origin 


Indonesia 


792 


Lower-Middle Income" 


French Origin 


Ireland 


13,070 


High Income 


English Origin 


Italy 


19,500 


High Income 


French Origin 


Japan 


32,232 


High Income 


German Origin 


South Korea 


7,830 


Upper-Middle Income 


German Origin 


Malaysia 


3,180 


Upper-Middle Income" 


English Origin 


Mexico 


3,530 


Upper-Middle Income 


French Origin 


Netherlands 


21,322 


High Income 


French Origin 


New Zealand 


13,030 


High Income 


English Origin 


Norway 


26,812 


High Income 


Scandinavian Origin 


Pakistan 


432 


Low Income 


English Origin 


Philippines 


878 


Lower-Middle Income 


French Origin 


Portugal 


8,350 


High Income" 


French Origin 


Singapore 


20,266 


High Income 


English Ongin 


South Africa 


2,890 


Upper-Middle Income" 


English Origin 


Spain 


13,430 


High Income 


French Origin 


Sweden 


24,960 


High Income 


Scandinavian Origin 


Switzerland 


36,800 


High Income 


German Origin 


Taiwan 


10,874 


High Income 


German Origin 


Thailand 


2,110 


Lower-Middle Income 


English Origin 


Turkey 


2,404 


Lower-Middle Income" 


French Origin 


United Kingdom 


17,974 


High Income 


English Origin 


United States 


24,758 


High Income 


English Origin 



Average per-capita GNP (US $) is the five year arithmetic average of per-capita GNP from 1991 
1995. The World Bank income classifications are obtained from the World Tables. The legal 
system classification identifies the legal origin of the Company Law or Commercial Code of each 
country. The legal system classifications are obtained from La Porta, Lopez-de-Silanes, Shleifer, 
and Vishny( 1997). 

' The World Bank income classifications varied across years for the following countries: Chile 
(lower-middle income in 1991), Indonesia (low income in 1991), Malaysia (lower-middle income 
in 1991), Portugal (upper-middle income in 1991 and 1992), South Africa (lower-middle income 
in 1991), Turkey (upper-middle income in 1993). 



35 



Table 2-2 
Firm Level Summary Statistics by Development Classifications and Legal System for Single- 
Segment and Multi-Segment Firms: 1991 - 1995 



Panel A: 


Firm Level Characteristics by World Bank Market Classifications 


Firm Level 

Characteristics by 

Development 

Classifications 


Single-Segment Firms 


Multi-Segment Firms 


Statistical Differences 
(p-values) 


Median 


Mean 


Median 


Mean 


Median 


Mean 


High Income 














Number of Assets 


1.000 


1.000 


2.000 


2.554 


0.000 


0.000 


Total Assets 
(mil $) 


276 


1755 


641 


1906 


0.000 


0.015 


Total Capital 
(mil $) 


180 


1727 


380 


1844 


0.000 


0.126 


Leverage Ratio 


0.265 


0.326 


0.287 


0.374 


0.714 


0.633 


Operating 
Income/Sales 


0.117 


0.134 


0.104 


0.115 


0.608 


0.527 


Capital 
Expenditure/Sales 


0.049 


0.111 


0.046 


0.078 


0.872 


0.389 


Market/Sales 


1.042 


1.738 


0.844 


1.211 


0.073 


0.054 


Observations 


17,366 


17,366 


8,159 


8,159 






Upper-Middle 
Income 














Number of 
Segments 


1.000 


1.000 


3.000 


2.958 


0.000 


0.000 


Total Assets 
(mil $) 


435 


1620 


931 


2513 


0.000 


0.000 


Total Capital 
(mil $) 


460 


1453 


664 


1776 


0.047 


0.000 


Leverage Ratio 


0.149 


0.217 


0.180 


0.225 


0.526 


0.782 


Operating 
Income/Sales 


0.148 


0.167 


0.145 


0.166 


0.813 


0.938 


Capital 
Expenditure/Sales 


0.083 


0.172 


0.090 


0.167 


0.726 


0.739 


Market/Sales 


1.385 


2.348 


1.575 


1.732 


0.107 


0.061 


Observations 


1,209 


1,209 


336 


336 







36 







Panel A-continued 








Firm Level 

Characteristics by 

Development 

Classifications 


Single-Segment Firms 


Multi-Segment Firms 


Statistical Differences 
(p-values) 


Median 


Mean 


Median 


Mean 


Median 


Mean 


Lower-Middle 
Income 














Number of 
Segments 


1.000 


1.000 


3.000 


2.684 


0.000 


0.000 


Total Assets 
(mil $) 


329 


1769 


610 


2571 


0.013 


0.000 


Total Capital 
(mil S) 


209 


1571 


412 


1531 


0.000 


0.562 


Leverage Ratio 


0.174 


0.268 


0.199 


0.231 


0.732 


0.824 


Operating 
Income/Sales 


0.185 


0.195 


0.190 


0.205 


0.824 


0.879 


Capital 
Expenditure/Sales 


0.101 


0.227 


0.067 


0.255 


0.213 


0.307 


Market/Sales 


1.595 


2.459 


1.295 


2.371 


0.114 


0.331 


Observations 


937 


937 


79 


79 






Low Income 














Number of 
Segments 


1.000 


1.000 


3.000 


2.833 


0.000 


0.000 


Total Assets 
(mil $) 


284 


1452 


838 


2480 


0.000 


0.000 


Total Capital 
(mil $) 


174 


1101 


545 


1659 


0.000 


0.000 


Leverage Ratio 


0.352 


0.388 


0.425 


0.393 


0.431 


0.917 


Operating 
Income/Sales 


0.149 


0.172 


0.127 


0.142 


0.267 


0.169 


Capital 
Expenditure/Sales 


0.075 


0.168 


0.084 


0.188 


0.698 


0.544 


Market/Sales 


1.402 


1.948 


1.414 


1.711 


0.329 


0.122 


Observations 


710 


710 


90 


90 













37 





Panel B: Firm Level Characteristics by Legal Sysl 


ems 




Firm Level 

Characteristics by 

Legal Systems 


Single-Segment Firms 


Multi-Segment Firms 


Statistical Differences 
(p- values) 


Median 


Mean 


Median 


Mean 


Median 


Mean 


English Origin 














Number of Assets 


1.000 


1.000 


2.000 


2.554 


0.000 


0.000 


Total Assets 
(mil $) 


276 


1755 


641 


1906 


0.000 


0.015 


Total Capital 
(mil $) 


180 


1727 


380 


1844 


0.000 


0.126 


Leverage Ratio 


0.265 


0.326 


0.287 


0.374 


0.714 


0.633 


Operating 
Income/Sales 


0.117 


0.134 


0.104 


0.115 


0.608 


0.527 


Capital 
Expenditure/Sales 


0.049 


0.111 


0.046 


0.078 


0.872 


0.389 


Market/Sales 


1.042 


1.738 


0.844 


1.211 


0.073 


0.054 


Observations 


17,366 


17,366 


8,159 


8,159 






French Origin 














Number of 
Segments 


1.000 


1.000 


3.000 


2.958 


0.000 


0.000 


Total Assets 
(mil $) 


435 


1620 


931 


2513 


0.000 


0.000 


Total Capital 
(mil $) 


460 


1453 


664 


1776 


0.047 


0.000 


Leverage Ratio 


0.149 


0.217 


0.180 


0.225 


0.526 


0.782 


Operating 
Income/Sales 


0.148 


0.167 


0.145 


0.166 


0.813 


0.938 


Capital 
Expenditure/Sales 


0.083 


0.172 


0.090 


0.167 


0.726 


0.739 


Market/Sales 


1.385 


2.348 


1.575 


1.732 


0.107 


0.061 


Observations 


1,209 


1,209 


336 


336 








38 



Panel B— continued 



Firm Level 

Characteristics by 

Legal Systems 


Single-Segment Firms 


Multi- Segment Firms 


Statistical Differences 
(p- values) 


Median 


Mean 


Median 


Mean 


Median 


Mean 


German Origin 














Number of 
Segments 


1.000 


1.000 


3.000 


2.684 


0.000 


0.000 


Total Assets 
(mil $) 


329 


1769 


610 


2571 


0.013 


0.000 


Total Capital 
(mil $) 


209 


1571 


412 


1531 


0.000 


0.562 


Leverage Ratio 


0.174 


0.268 


0.199 


0.231 


0.732 


0.824 


Operating 
Income/Sales 


0.185 


0.195 


0.190 


0.205 


0.824 


0.879 


Capital 
Expenditure/Sales 


0.101 


0.227 


0.067 


0.255 


0.213 


0.307 


Market/Sales 


1.595 


2.459 


1.295 


2.371 


0.114 


0.331 


Observations 


937 


937 


79 


79 






Scandinavian 
Origin 














Number of 
Segments 


1.000 


1.000 


3.000 


2.833 


0.000 


0.000 


Total Assets 
(mil $) 


284 


1452 


838 


2480 


0.000 


0.000 


Total Capital 
(mil $) 


174 


1101 


545 


1659 


0.000 


0.000 


Leverage Ratio 


0.352 


0.388 


0.425 


0.393 


0.431 


0.917 


Operating 
Income/Sales 


0.149 


0.172 


0.127 


0.142 


0.267 


0.169 


Capital 
Expenditure/Sales 


0.075 


0.168 


0.084 


0.188 


0.698 


0.544 


Market/Sales 


1.402 


1.948 


1.414 


1.711 


0.329 


0.122 


Observations 


710 


710 


90 


90 






In Panel A, firms are 


classified ea 


ch vear bv th 


eir countrv's 


World Rank 


market class 


ifiratinn 



•* * J — — -■■■■■ « ■ ■ VaWWHnWHVM) 

while in Panel B firms are classified by their country's legal system. Single-segment firms are 
firms that operate in only one two-digit SIC code industry. Multi-segment firms are defined as 
firms that operate in two or more two-digit SIC code industries and no firm segment sales exceed 
90% of total firm sales. The leverage ratio is defined as book value of debt divided by total 
assets. Market-to-sales is defined as the ratio of a firm's market value of equity plus book value 
of debt to its total sales. 



39 



Table 2-3 

Excess Values by Development Groups, Broader Per-Capita GNP Groups and Legal Systems for 

Single-Segment and Multi-Segment Firms: 1991 - 1995 



Panel A: Excess Values by World Bank Market Classification for Single-Segment and Multi- 
Segment Firms 



Firm Level 

Characteristics by 

Development 

Classification 


Single-Segment 
Firms 


Multi-Segment Firms 


Statistical 

Differences 

(p-values) 


Median 


Mean 


Median 


Mean 


Median 


Mean 


High Income 


0.0000 


0.0199 


-0.0576 


-0.0584 


0.000 


0.000 


Upper-Middle Income 


0.0000 


0.0070 


-0.0722 


-0.0181 


0.051 


0.398 


Lower-Middle Income 


0.0000 


0.0330 


0.0863 


0.0543 


0.032 


0.721 


Low Income 


0.0000 


0.0100 


0.0380 


0.0945 


0.161 


0.005 


Observations 














High Income 


17,366 


17,366 


8,159 


8,159 






Upper-Middle Income 


1,209 


1,209 


336 


336 






Lower-Middle Income 


937 


937 


79 


79 






Low Income 


710 


710 


90 


90 







Panel B: Excess Values by Per-Capita GNP for Single-Segment and Multi-Segment Firms 


Excess Values 
by Per-Capita GNP 


Single-Segment 
Firms 


Multi-Segment Firms 


Statistical 

Differences 

(p-values) 


Median 


Mean 


Median 


Mean 


Median 


Mean 


Per-Capita GNP > 
$15,000 


0.0000 


0.0211 


-0.0578 


-0.0579 


0.000 


0.000 


$15,000 > Per-Capita 
GNP > $5,000 


0.0000 


-0.0026 


-0.0542 


-0.0281 


0.136 


0.488 


Lo$5,000> Per-Capita 
GNP > $1,000 


0.0000 


0.0260 


-0.0400 


-0.0264 


0.148 


0.112 


$1,000 > Per-Capita 
GNP 


0.0000 


0.0068 


0.0541 


0.0841 


0.101 


0.014 


Observations 














Per-Capita GNP > 
$15,000 


16,543 


16,543 


8,072 


8,072 






$15,000 > Per-Capita 
GNP > $5,000 


1,069 


1,069 


164 


164 






Lo$5,000 > Per-Capita 
GNP > $1,000 


1,643 


1,643 


306 


306 






$1,000 > Per-Capita 
GNP 


967 


967 


122 


122 







40 



Panel C: Excess Values by Legal Systems for Single-Segment and Multi-Segment Firms 


Excess Values 
by Legal Systems 


Single-Segment 
Firms 


Multi-Segment Firms 


Statistical 

Differences 

(p-values) 


Median 


Mean 


Median 


Mean 


Median 


Mean 


English Origin 


0.0000 


0.0088 


-0.0576 


-0.0584 


0.000 


0.000 


French Origin 


0.0000 


0.0287 


-0.0722 


-0.0181 


0.051 


0.398 


German Origin 


0.0000 


0.0322 


0.0863 


0.0543 


0.032 


0.721 


Scandinavian Origin 


0.0000 


0.0050 


0.0380 


0.0945 


0.161 


0.005 


Observations 














English Origin 


14,931 


14,931 


6,207 


6,207 






French Origin 


2,378 


2,378 


843 


843 






German Origin 


2,108 


2,108 


1,290 


1,290 






Scandinavian Origin 


683 


683 


368 


368 






In Panel A, firms are clas 


sified each \ 


'ear bv their 


countrv's W 


'orld Rank n 


narket classi 


fi cation 



while in Panel B firms are classified by broader per-capita GNP groups. In Panel C, firms are 
classified by their country's legal system. Excess value is defined as the natural logarithm of the 
ratio of a firm's market-to-sales ratio to its imputed market-to-sales ratio. Firms with excess 
values that are greater than four or less than one-fourth are eliminated from the sample. Single- 
segment firms are firms that operate in only one two-digit SIC code industry. Multi-segment 
firms are defined as firms that operate in two or more two-digit SIC code industries and no firm 
segment sales exceed 90% of total firm sales. 



41 



Table 2-4 
Country Level Regression Estimates of Excess Values: 1991 - 1995 



Country 


Constant 


SEG 


OIS 


CES 


ASSETS 


AdjR 2 


Obs 


Australia 


-0.520* 
(-1.810) 


-0.152*** 
(-3.021) 


0.192* 
(1.859) 


0.328*** 
(4.236) 


0.022 
(1.464) 


0.059 


596 


Austria 


0.666 
(0.942) 


-0.211** 
(-1.970) 


0.661** 
(2.163) 


0.074 
(0.252) 


-0.035 
(-1.063) 


0.058 


129 


Brazil 


-1.447*** 
(-4.417) 


-0.075 
(-0.700) 


0.141 
(0.730) 


-0.031 
(-1.511) 


0.124*** 
(4.601) 


0.080 


245 


Canada 


-1.468*** 
(-7.616) 


-0.059* 
(-1.665) 


0.027 
(0.430) 


0.192*** 
(5.701) 


0.073*** 
(7.413) 


0.062 


1,315 


Chile 


-1.128* 
(-1.830) 


-0.289 
(-1.300) 


-0.016 
(-0.534) 


0.048*** 
(2.533) 


0.053** 
(2.225) 


0.091 


118 


China 


0.392 
(0.307) 


0.221 
(0.447) 


0.065 
(0.158) 


0.275 
(1.366) 


-0.019 
(-0.323) 


0.000 


78 


Denmark 


-0.165 
(-0.398) 


-0.063 
(-0.937) 


2.016*** 
(5.432) 


0.619*** 
(3.925) 


-0.003 
(-0.163) 


0.137 


270 


Finland 


-0.356 
(-1.291) 


-0.016 
(-0.282) 


0.881*** 
(2.939) 


0.448** 
(2.119) 


0.012 
(0.828) 


0.075 


209 


France 


-0.953*** 
(-4.924) 


-0.085*** 
(-2.502) 


-0.004 
(-0.539) 


0.052 
(1.378) 


0.052*** 
(5.821) 


0.038 


1,131 


Germany 


0.212 
(1.270) 


-0.050 
(-1.568) 


0.807*** 
(7.054) 


0.223*** 
(3.370) 


-0.009 
(-1.094) 


0.039 


1,296 


Hong 
Kong 


-0.235 
(-0.574) 


0.145*** 
(2.786) 


0.214 
(1.176) 


0.065 
(0.838) 


0.005 
(0.271) 


0.021 


374 


India 


-1.940*** 
(-5.355) 


-0.011 
(-0.175) 


2.184*** 
(9.863) 


0.260*** 
(3.491) 


0.080*** 
(5.335) 


0.235 


553 


Indonesia 


-1.927*** 
(-2.715) 


-0.104 
(-1.011) 


0.400 
(1.381) 


0.236*** 
(2.892) 


0.082*** 
(2.896) 


0.117 


218 


Ireland 


-1.646*** 
(-4.148) 


-0.003 
(-0.037) 


0.377*** 
(2.543) 


0.074*** 
(2.585) 


0.084*** 
(3.858) 


0.116 


179 


Italy 


1.101* 
(1.917) 


0.073 
(1.099) 


0.322 
(0.948) 


0.293 
(0.978) 


-0.041* 
(-1.935) 


0.015 


259 


Japan 


2.658*** 
(11.772) 


-0.039* 
(-1.635) 


2.908*** 
(12.204) 


-0.697*** 
(-2.760) 


-0.108*** 
(-12.588) 


0.237 


1,137 


Malaysia 


0.864** 
(2.484) 


0.063 
(1.274) 


1.172*** 
(6.343) 


0.121* 
(1.690) 


-0.046*** 
(-2.583) 


0.103 


527 


Mexico 


-0.342 
(-0.439) 


-0.102 0.168 
(-1.029) (0.530) 


0.412 
(1.289) 


0.007 
(0.201) 


0.021 


108 









42 



Table 2-4— continued 



Country 


Constant 


SEG 


OIS 


CES 


ASSETS 


Adj 
R 2 


Obs 


Netherlands 


-0.787*** 
(-2.819) 


-0.040 
(-0.798) 


1.406*** 
(4.446) 


-0.165 
(-0.668) 


0.039*** 
(2.839) 


0.067 


388 


New 
Zealand 


0.773 
(1.408) 


-0.214* 
(-1.942) 


0.756** 
(2.511) 


-0.584 
(-0.549) 


-0.446 
(-1.579) 


0.035 


101 


Norway 


0.377 
(0.757) 


0.172** 
(2.145) 


0.544*** 
(2.793) 


0.347*** 
(4.889) 


-0.023 
(-0.945) 


0.137 


235 


Pakistan 


-0.269 
(-0.380) 


0.606*** 

(3.188) 


0.868*** 
(2.694) 


0.021 
(0.083) 


0.013 
(0.406) 


0.153 


134 


Philippines 


-0.478 
(-0.598) 


0.430 
(1.297) 


0.064 
(0.430) 


0.116 
(1.068) 


0.021 
(0.556) 


0.000 


97 


Portugal 


-1.205 
(-1.166) 


0.031 
(0.094) 


0.774* 
(1.902) 


0.353 
(0.886) 


0.044 
(1.057) 


0.013 


84 


Singapore 


-0.323 
(-0.906) 


0.132*** 
(2.570) 


0.561*** 
(2.987) 


0.110 
(1.019) 


0.004 
(0.224) 


0.064 


368 


South Korea 


1.349** 
(2.011) 


0.066 
(1.057) 


0.324 
(1.000) 


0.643*** 
(3.613) 


-0.052** 
(-2.052) 


0.053 


264 


South 
Africa 


0.178 
(0.423) 


-0.072 
(-1.032) 


1.860*** 
(7.221) 


0.411*** 
(4.710) 


-0.030 
(-1.452) 


0.184 


305 


Spain 


-0.393 
(-0.800) 


-0.307*** 
(-3.206) 


0.327** 
(2.171) 


0.104 
(0.654) 


0.013 
(0.620) 


0.043 


320 


Sweden 


-0.963*** 

(-2.784) 


-0.174*** 
(-3.066) 


1.327*** 
(3.509) 


-0.174 
(-1.040) 


0.048*** 
(3.093) 


0.111 


337 


Switzerland 


-1.067*** 
(-3.104) 


0.016 
(0.313) 


1.180*** 
(4.623) 


0.239 
(1.144) 


0.048*** 
(2.881) 


0.107 


358 


Taiwan 


0.697 
(1.104) 


0.170 
(1.239) 


0.743*** 
(3.521) 


0.096 
(0.566) 


-0.032 
(-1.208) 


0.064 


214 


Thailand 


-0.325 
(-0.774) 


-0.094 
(-0.659) 


0.069 

(1.245) 


0.126** 
(2.506) 


0.021 
(1.055) 


0.022 


460 


Turkey 


-1.114 
(-0.901) 


-0.688* 
(-1.653) 


-0.201 
(-0.531) 


1.520*** 
(4.037) 


0.033 
(0.772) 


0.247 


67 


United 
Kingdom 


-0.572*** 
(-6.744) 


-0.067*** 
(-3.734) 


0.251*** 
(6.470) 


0.575*** 
(10.742) 


0.029*** 
(6.221) 


0.056 


4,951 


United 
States 


-0.250*** 
(-3.827) 


-0.132*** 
(-10.548) 


0.393*** 
(13.216) 


0.596*** 
(15.383) 


0.008** 
(2.475) 


0.059 


11,461 


Significant at 1 


percent (*** 


), 5 percent ( 


"*), andlOpe 


:rcent (*) lev< 


:1s. 







We estimate the following regression model from 1991-1995 for each of the thirty-five individual 
countries in our sample: 

Excess Value = a + P, (Diversification Dummy) + (^ (Log Assets) + fc (Operating Income / 
Sales) + p 4 (Capital Expenditures / Sales) + e. 

Excess value is defined as the natural logarithm of the ratio of a firm's market-to-sales ratio to its 
imputed market-to-sales ratio. Firms with excess values that are greater than four or less than 
one-fourth are eliminated from the sample. The diversification dummy, SEG, is equal to one for 
multi-segment firms and zero otherwise. Multi-segment firms are defined as firms that operate in 
two or more two-digit SIC code industries and no firm segment sales exceed 90% of total firm 
sales. OIS is defined as the firm's operating income-to-sales, while CES is the firm's capital 



43 



expenditures-to-sales. Assets are defined as the natural logarithm of the firm's total assets. The 
regressions also include year dummies for 1992-1995. 









44 



Table 2-5 
Firm Level Regression Estimates of Excess Values: 1991 



1995 



Variables 


OLS 

(1) 


OLS 

(2) 


OLS 

(3) 


Fixed Effects 
(4) 


Constant 


-0.270*** 
(-10.677) 


-0.232*** 
(-8.726) 


-0.240*** 
(-9.040) 






Multi-Segment 
Dummy (SEG) 


-0.078*** 
(-10.748) 


-0.096*** 
(-11.584) 


-0.004 
(-0.201) 


0.037 
(1.048) 


Operating Income- 
to-Sales (OIS) 


0.042*** 
(6.540) 


0.043*** 
(6.702) 


0.043*** 
(6.701) 


-0.006 

(-0.372) 


Capital 

Expenditures-to- 
Sales (CES) 


0.226*** 
(18.982) 


0.226*** 
(18.982) 


0.225*** 
(18.898) 


0.144*** 
(11.733) 


Log of Total 
Assets (ASSETS) 


0.014*** 
(11.424) 


0.012*** 
(9.343) 


0.012*** 
(9.565) 


-0.027*** 
(-5.571) 


Per-Capita GNP 
(GNPCAP*SEG) a 






-0.426*** 

(-4.252) 


-0.682*** 
(-4.423) 






Low Income 

Dummy 

(G1*SEG) 




0.142** 
(2.398) 












Lower-Middle 
Income Dummy 
(G2*SEG) 




0.036 
(0.564) 












Upper-Middle 
Income Dummy 
(G3*SEG) 




0.002 
(0.063) 












French Legal 
System Dummy 
(FRENCH*SEG) 




0.052*** 
(2.509) 


0.047** 
(2.268) 


-0.033 
(-0.703) 




German Legal 
System Dummy 
(GERMAN*SEG) 




0.072*** 
(4.030) 


0.099*** 
(5.170) 


0.111*** 
(2.763) 




Scandinavian 
Legal System 
Dummy 
(SCAND*SEG) 




0.044 
(1.449) 


0.058* 
(1.919) 


0.013 
(0.226) 




Adjusted R 2 


0.025 


0.025 


0.026 


0.027 


Number of 
Observations 


28,886 


28,886 


28,886 


28,886 



' coefficient estimate x 10" 



Regression estimates are from 1991-1995. Excess value is defined as the natural logarithm of the 
ratio of a firm's market-to-sales ratio to its imputed market-to-sales ratio. Firms with excess 
values that are greater than four or less than one-fourth are eliminated from the sample. The 
diversification dummy, SEG, is equal to one for multi-segment firms and zero otherwise. Multi- 
segment firms are defined as firms that operate in two or more two-digit SIC code industries and 
no firm segment sales exceed 90% of total firm sales. SEGN is the number of firm segments 
defined at the two-digit SIC code level. GNPCAP is the annual per-capita GNP of the country 
where the firm is headquartered. G1-G3 are dummy variables corresponding to each of the 



45 



World Bank income groups. French, German, and Scandinavian are dummy variables 
corresponding to each legal system. The dummy variables are equal to one for each 
corresponding classification and zero otherwise. Per-capita GNP, the World Bank income group 
dummies, and the legal system dummies are interacted with the multi-segment dummy (SEG) for 
the all firms panel and the number of segments (SEGN) for the multi-segment firms panel. 
Models 1-3 are estimated over 1991-1995 using ordinary least squares. Column (4) provides 
fixed-effects estimates (within-firm estimates) of Model 3. Each model specification also 
includes year dummies for 1992-1995. 






46 



Table 2-6 

Firm Level Regression Estimates of Excess Values using Additional Proxies for Capital Market 

Development and the Legal Environment: 1991 - 1995 



Variables 


OLS 

(1) 


Fixed Effects 
(2) 


OLS 

(3) 


Fixed Effects 
(4) 


Constant 


-0.237*** 
(-8.665) 




-0.226*** 
(-8.435) 








Multi- Segment 
Dummy (SEG) 


-0.164** 
(-1.942) 


-0.156 
(-1.404) 


-0.063** 
(-2.099) 


-0.103* 
(-1.725) 


Operating Income- 
to-Sales (OIS) 


0.042*** 
(6.636) 


0.011* 
(1.688) 


0.042*** 
(6.645) 


0.010* 
(1.657) 


Capital 

Expenditures-to- 
Sales (CES) 


0.223*** 
(18.769) 


0.260*** 

(11.673) 


0.224*** 
(18.804) 


0.260*** 
(11.664) 


Log of Total 

Assets (ASSETS) 


0.012*** 
(9.279) 


0.007*** 
(3.136) 


0.012*** 
(9.086) 


0.007*** 
(3.234) 


Per-Capita GNP 
(GNPCAP*SEG) a 


-0.420** 
(-2.131) 


-0.467** 
(-2.099) 










[(MKTCAP + 
Debt)/GNP]*SEG 


-0.054** 
(-1.976) 


-0.069* 
(-1.684) 


-0.054** 
(-2.352) 


-0.056** 
(-2.031) 


(Domestic 
Firms/Pop)*SEG 


-0.002*** 
(-3.150) 


-0.002** 
(-2.404) 


-0.002*** 

(-2.743) 


-0.003** 
(-1.983) 


(IPOs/Pop)*SEG 


0.013 
(1.083) 


0.005 
(0.639) 


0.016 

(1.345) 


0.003 
(0.537) 


French Legal 
System Dummy 
(FRENCH* SEG) 


0.066*** 

(4.779) 


0.035** 
(2.341) 


0.058*** 
(4.574) 


0.040** 
(2.185) 


German Legal 
System Dummy 
(GERMAN*SEG) 


0.082*** 
(5.284) 


0.042** 
(2.347) 


0.061*** 
(4.893) 


0.059*** 
(2.991) 


Scandinavian 
Legal System 
Dummy 
(SC AND* SEG) 


0.034*** 
(3.679) 


0.041** 
(2.254) 


0.033*** 
(3.633) 


0.031** 
(2.010) 


(RuleofLaw)*SEG 


-0.005 
(-0.464) 


-0.024 
(-1.422) 


-0.025*** 
(-4.017) 


-0.016** 
(-2.322) 


Adjusted R 2 


0.027 


0.040 


0.026 


0.038 


Number of 
Observations 


27,132 


27,132 


27,132 


27,132 



*), 5 percent (**), and 10 percent (*) levels. 
a coefficient estimate x 10" 5 



Regression estimates are from 1991-1995. Excess value is defined as the natural logarithm of the 
ratio of a firm's market-to-sales ratio to its imputed market-to-sales ratio. Firms with excess 
values that are greater than four or less than one-fourth are eliminated from the sample. The 
diversification dummy, SEG, is equal to one for multi-segment firms and zero otherwise. Multi- 
segment firms are defined as firms that operate in two or more two-digit SIC code industries and 
no firm segment sales exceed 90% of total firm sales. GNPCAP is the annual per-capita GNP of 
the country where the firm is headquartered. French, German, and Scandinavian are dummy 



47 



variables corresponding to each legal system. The dummy variables are equal to one for each 
corresponding classification and zero otherwise. From LaPorta, Lopez-De-Silanes, Shleifer, and 
Vishny (1997), we obtain for each country the ratio of the stock market capitalization held by 
minorities plus the sum of bank debt of the private sector and outstanding non-financial bonds to 
GNP (MKTCAP + Debt/GNP), the ratio of the number of domestic firms listed in a given country 
to its population (Domestic Firms/Pop), and the ratio of the number of the initial public offerings 
of equity in a given country to its population (IPOs/Pop). From LLSV, we also obtain the law 
and order tradition (Rule of Law) in each country. Columns 1 and 3 provide OLS estimates over 
1991-1995, while columns 2 and 4 provide fixed-effects estimates (within-firm estimates). Each 
specification also includes year dummies for 1992-1995. 



48 



Table 2-7 
Descriptive Statistics on Ownership Concentration by Economic Development and Legal System: 

1991-1995 



Panel A: Ownership Concentration for Subset of Firms Reporting Ownership Holdings Greater or 
Equal to 5% 



Groups 


Total 


Single-Segment Firms 


Mean 


Median 


Mean 


Median 


Economic 
Development: 










High Income 


0.40 


0.38 


0.41 


0.39 


Upper-Middle 
Income 


0.52 


0.54 


0.54 


0.57 


Lower-Middle 
Income 


0.63 


0.65 


0.64 


0.66 


Low Income 


0.59 


0.60 


0.58 


0.60 


Legal System: 










English Origin 


0.39 


0.36 


0.40 


0.38 


French Origin 


0.58 


0.60 


0.59 


0.61 


German Origin 


0.43 


0.38 


0.44 


0.42 


Scandinavian 
Origin 


0.44 


0.44 


0.45 


0.46 



49 



Panel A— continued 



Groups 


Multi-Segment Firms 


% of Total Sample Reporting 
Ownership 


Mean 


Median 


Economic 
Development: 








High Income 


0.38 


0.34 


65% 


Upper-Middle 
Income 


0.46 


0.46 


78% 


Lower-Middle 
Income 


0.61 


0.61 


34% 


Low Income 


0.65 


0.69 


14% 


Legal System: 








English Origin 


0.36 


0.32 


61% 


French Origin 


0.55 


0.56 


64% 


German Origin 


0.40 


0.35 


72% 


Scandinavian Origin 


0.42 


0.40 


73% 



50 



Panel B: Ownership Concentration Set Equal to Zero where Not Reported 



Groups 


Total 


Single-Segment Firms 


Multi-Segment Firms 




Mean 


Median 


Mean 


Median 


Mean 


Median 


Economic 
Development: 














High Income 


0.32 


0.29 


0.33 


0.31 


0.30 


0.26 


Upper-Middle 
Income 


0.43 


0.48 


0.44 


0.51 


0.41 


0.44 


Lower-Middle 
Income 


0.22 


0.00 


0.21 


0.00 


0.41 


0.00 


Low Income 


0.08 


0.00 


0.08 


0.00 


0.11 


0.00 


Legal System: 














English Origin 


0.29 


0.27 


0.30 


0.28 


0.28 


0.24 


French Origin 


0.44 


0.51 


0.43 


0.51 


0.47 


0.51 


German Origin 


0.33 


0.24 


0.33 


0.22 


0.32 


0.24 


Scandinavian 
Origin 


0.39 


0.39 


0.41 


0.42 


0.37 


0.36 


Worldscope provi< 


ies firm level 


ownership d< 


ita that consii 


;ts of reportec 


cases where 


an 



individual or institution holds at least five percent of a company's common stock. Summing up 
these reported holdings, we obtain ownership concentration. We use two different methods to 
classify the unreported ownership data. In the first method (Panel A), we treat the unreported 
observations as missing values. In the second method (Panel B), we treat the unreported 
observations as zero values. 



51 



Table 2-8 
Firm Level Regression Estimates of Excess Values Controlling for Ownership Concentration: 

1991-1995 



Variables 


Subset of Firms Reporting Ownership Concentration Greater or Equal 

to 5% 


OLS 
(1) 


OLS 

(2) 


Fixed Effects 
(3) 


Constant 


-0.073 
(-1.223) 


-0.065 
(-1.088) 






Multi-Segment 
Dummy (SEG) 


-0.041 
(-1.504) 


-0.085** 
(-2.392) 


-0.084** 
(-2.400) 


Operating Income-to- 
Sales (OIS) 


0.350*** 
(16.700) 


0.349*** 
(16.662) 


0.351*** 
(16.718) 


Capital Expenditures- 
to-Sales (CES) 


0.213*** 
(13.287) 


0.213*** 
(13.302) 


0.212*** 
(13.309) 


Log of Total Assets 
(ASSETS) 


0.005*** 
(3.438) 


0.006*** 
(3.632) 


0.006*** 
(3.646) 


Per-Capita GNP 
(GNPCAP*SEG) a 


-0.378*** 
(-3.072) 


-0.327*** 
(-2.619) 


-0.329*** 
(-2.644) 


French Legal System 

Dummy 

(FRENCH*SEG) 


0.086*** 
(3.358) 


0.072*** 
(2.747) 


0.072*** 
(2.735) 


German Legal System 

Dummy 

(GERMAN*SEG) 


0.136*** 
(5.935) 


0.128*** 
(5.528) 


0.129*** 
(5.573) 


Scandinavian Legal 
System Dummy 
(SC AND* SEG) 


0.097** 
(2.712) 


0.089*** 
(2.492) 


0.088*** 
(2.493) 


Ownership 
Concentration < 10 
(OWNOtolO) 


0.079 
(0.145) 


0.055 
(0.102) 


0.051 
(0.094) 


Ownership 
Concentration 10-30 
(OWN10to30) 


-0.289*** 
(-3.387) 


-0.336*** 
(-3.416) 


-0.344*** 
(-3.500) 


Ownership 
Concentration > 30 
(OWNover30) 


-0.054** 
(-1.968) 


-0.077** 
(-2.373) 


-0.068** 
(-2.129) 


Ownership 
Concentration 10-30 
interacted with SEG 
(OWN10to30*SEG) 




0.142 
(0.950) 


0.155 
(1.040) 




Ownership 
Concentration > 30 
interacted with SEG 
(OWNover30*SEG) 




0.089 
(1.468) 


0.081 
(1.343) 




Adjusted R 2 


0.040 


0.041 


0.042 


Number of 
Observations 


18,225 


18,225 


18,225 






52 



Table 2-8 — continued 



Variables 


Ownership Concentration Set Equal to Zero where Not Reported 


OLS 
(4) 


OLS 

(5) 


Fixed Effects 
(6) 


Constant 


-0.126*** 

(-4.078) 


-0.134*** 
(-4.341) 






Multi-Segment 
Dummy (SEG) 


-0.026 
(-1.137) 


-0.007 
(-0.258) 


-0.006 
(-0.249) 


Operating Income-to- 
Sales (OIS) 


0.303*** 
(17.138) 


0.303*** 
(17.142) 


0.304*** 
(17.185) 


Capital Expenditures- 
to-Sales (CES) 


0.160*** 
(12.224) 


0.160*** 
(12.254) 


0.161*** 

(12.250) 


Log of Total Assets 
(ASSETS) 


0.006*** 
(4.368) 


0.006*** 
(4.450) 


0.006*** 
(4.466) 


Per-Capita GNP 
(GNPCAP*SEG) a 


-0.336*** 
(-3.237) 


-0.331*** 
(-3.175) 


-0.331*** 
(-3.200) 


French Legal System 

Dummy 

(FRENCH*SEG) 


0.067*** 
(2.887) 


0.063*** 
(2.670) 


0.063*** 
(2.656) 


German Legal System 

Dummy 

(GERMAN*SEG) 


0.107*** 
(5.292) 


0.102*** 
(4.999) 


0.103*** 
(5.039) 


Scandinavian Legal 
System Dummy 
(SCAND*SEG) 


0.079** 
(2.439) 


0.080*** 
(2.469) 


0.080*** 
(2.468) 


Ownership 
Concentration < 1 
(OWNOtolO) 


0.550*** 
(3.915) 


0.537*** 
(3.815) 


0.533*** 
(3.788) 


Ownership 
Concentration 10-30 
(OWN10to30) 


-0.313*** 
(-4.033) 


-0.213*** 
(-2.479) 


-0.219*** 
(-2.556) 


Ownership 
Concentration > 30 
(OWNover30) 


-0.043 
(-1.590) 


-0.083*** 
(-2.613) 


-0.074** 
(-2.364) 


Ownership 
Concentration 10-30 
interacted with SEG 
(OWN10to30*SEG) 




-0.300*** 
(-2.689) 


-0.289*** 
(-2.599) 




Ownership 
Concentration > 30 
interacted with SEG 
(OWNover30*SEG) 




0.132** 
(2.248) 


0.124** 
(2.129) 




Adjusted R 2 


0.035 


0.035 


0.036 


Number of 
Observations 


28,886 


28,886 


28,886 


Significant at 1 percent (* 


**), 5 percent (**), and 


10 percent (*) levels. a c 


:oefficient estimate x 10" 5 



Regression estimates are from 1991-1995. Excess value is defined as the natural logarithm of the 
ratio of a firm's market-to-sales ratio to its imputed market-to-sales ratio. Firms with excess 
values that are greater than four or less than one-fourth are eliminated from the sample. The 



53 



diversification dummy, SEG, is equal to one for multi-segment firms and zero otherwise. Multi- 
segment firms are defined as firms that operate in two or more two-digit SIC code industries and 
no firm segment sales exceed 90% of total firm sales. GNPCAP is the annual per-capita GNP of 
the country where the firm is headquartered. French, German, and Scandinavian are dummy 
variables corresponding to each legal system. The dummy variables are equal to one for each 
corresponding classification and zero otherwise. We use two different methods to classify 
unreported the ownership data. In the first method (columns 1-3), we treat the unreported 
observations as missing values. In the second method (columns 4-6), we treat the unreported 
observations as zero values. OWNOtolO: = total ownership if total ownership < 0.10, = 0.10 if 
total ownership > 0.10; OWN10to30: = if total ownership < 0.10, = total ownership minus 
0.10 if 0.10 < total ownership < 0.30, = 0.20 if total ownership > 0.30; OWNover30: = if 
total ownership < 0.30, = total ownership minus 0.30 if total ownership > 0.30. Column (3) and 
(6) provide fixed-effects estimates (within-firm estimates) of Model 2. Each model specification 
also includes year dummies for 1992-1995. 









CHAPTER 3 
FIRM VALUE AND INTERNATIONAL DIVERSIFICATION 

Introduction 

Over the past twenty-five years, foreign investment by corporations in the industrialized 
nations has grown dramatically. Specifically, net foreign investment by firms in OECD countries 
has grown from around $9 billion U.S. dollars in 1975 to $154 billion U.S. dollars in 1997 
(Global Development Finance, 1999). Indeed, for many companies today, foreign investment 
now represents a considerable portion of their overall sales and profits. 

This increase in foreign investment has occurred for a number of reasons. Most notably, 
lower transaction costs, improved communications, and increasingly integrated capital markets 
have lowered the cost of doing business in foreign markets. To the extent that firms are able to 
leverage their operations worldwide, international investment may enable them to capture 
valuable operating synergies. International diversification may also provide important financial 
synergies to the extent it is efficient for multinational firms to raise external capital and then 
allocate it among their various global operations using internal capital markets. Multinational 
firms also provide investors with a vehicle to diversify their investments internationally without 
having to directly invest in foreign markets, although it is unclear if any of this benefit accrues to 
the multinational itself. 

At the same time, firms often incur additional costs and risks when investing in foreign 
markets. While geographic diversification may reduce corporate risk, multinational firms also 
have to contend with exchange rate risk, political risk, and the costs incurred when managing 
resources over a larger geographic area. So while it is widely acknowledged that companies face 
additional benefits and costs when investing overseas, it is unclear whether geographically 



54 



55 

diversified firms trade at a premium or discount relative to firms that operate within a single 
market. 

Recently, several studies have found that U.S. firms that diversify along product lines 
trade at a discount relative to focused firms [e.g., Lang and Stulz (1994) and Berger and Ofek 
(1995)]. Other studies [e.g., Lins and Servaes (1999) and Fauver, Houston, and Naranjo (1999)] 
have found that this result extends to other industrialized countries. 1 However, many multi- 
product firms are also geographically diversified. Thus, it is difficult to disentangle the effects of 
product market diversification and geographic diversification without simultaneously controlling 
for the two effects. In particular, since the level of product market diversification influences firm 
value, it is important to also control for the level of product market diversification when 
evaluating the benefits of geographic diversification. 

The existing evidence regarding the corporate benefits of international diversification has 
so far yielded mixed and inconclusive results. 2 Bodnar, Tang, and Weintrop (1998), for example, 
use a large sample of U.S. firms over the time period from 1987 to 1993, and find that 
geographically diversified firms have higher values relative to comparable single-product 
domestic firms. Interestingly, they also find that the product market diversification discount 
becomes less pronounced after controlling for whether or not the firm is geographically 
diversified. Using Tobin's Q and a sample of U.S. firms, Morck and Yeung (1991, 1998) also 
find a positive relation with geographic diversification and a negative relation with industrial 
diversification. Similarly, Errunza and Senbet (1981, 1984) determine a positive relation between 
the degree of international involvement and excess value. On the other hand, Denis, Denis, and 



1 Fauver, Houston, and Naranjo (1999), however, find that these results do not necessarily extend 
to countries that have less developed capital markets. 

Many of the earlier studies, moreover, do not simultaneously control for the effects of product 
market and geographic diversification. For instance, Kim and Lyn (1986) find there exists a 
positive relation between the excess value of a multinational corporation and the degree of 
international involvement, but they do not simultaneously control for industry effects. 



56 

Yost (1999) find that geographic and industrial diversification, separately, lowers value, but that 
firms that diversify both industrially and geographically experience an increase in value. 

In this paper, we further investigate the connection between product and geographic 
diversification by examining firms that are headquartered in Germany, Japan, the U.K., and the 
U.S. We also extend earlier studies by controlling for the firm's ownership structure, since 
previous studies have found that a firm's ownership structure plays a significant role in affecting 
the value of corporate diversification (e.g., Denis, Denis, and Sarin (1997), Claessens, Djankov, 
Fan and Lang (1998) and Fauver, Houston, and Naranjo (1999)). Lastly, we also employ both 
domestic and international benchmarks in assessing the value of geographic diversification. 

In our analysis, we have collected data for more than 4,000 firms located in four 
industrialized countries (Germany, Japan, the U.K. and U.S.) over the time period 1991-1995. 
Using the methodology adopted by Berger and Ofek (1995), we calculate the implied value of 
geographic and product diversification. Our regression analysis also controls for the firm's size, 
profitability, capital intensity, and ownership structure. 

Our results suggest that product market diversification has a negative effect on firm value 
in three of the four countries (Japan, the U.K., and the U.S.). These results directly parallel the 
results reported by Lins and Servaes (1999) and Fauver, Houston, and Naranjo (1999), each of 
whom found that focused firms performed better in Japan, the U.K., and the U.S., while product 
market diversification had no significant effect on German firms. Moreover, after controlling for 
firm characteristics and the level of product market diversification, we find that multinational 
firms trade at a premium relative to firms that operate in a single domestic market in two of the 
four countries (Japan and the U.S.). These results confirm Bodnar, Tang, and Weintrop's 
evidence regarding U.S. firms, but they also suggest that the observed value of geographic 
diversification may not extend to multinationals throughout the world. 



Although we limit our study to these four countries, the firms within them operate in many 
countries throughout the world. 



57 

In one respect, these results are not surprising. When firms are ranked worldwide, 
Japanese and U.S. multinationals are typically among the largest and most profitable. At first 
glance, these results suggest that the average Japanese and U.S. multinational firm is able to 
generate valuable operating synergies, which leads them to be worth more than the simple sum of 
their individual parts. 

Interestingly, however, we also employ additional tests which indicate that while 
multinationals may dominate firms in their own domestic markets, they do no better than a 
matched portfolio of international firms. These results are consistent with the findings of several 
studies in the international investments literature which find that the risk-adjusted returns of 
multinationals are similar to that of a portfolio of individual stocks that have the same 
characteristics as the individual parts of the multinational (e.g., Heston and Rouwenhorst (1994), 
Griffin and Karolyi (1998), and Rowland and Tesar (1998)). 

The rest of this chapter proceeds as follows. The next section describes the data and 
methodology. Section three presents the unconditional results regarding the impact of product- 
market diversification and geographic diversification. Section four presents the regression results 
that control for various firm characteristics, including ownership concentration. In these 
regressions, firm value is calculated relative to a domestic benchmark; this enables us to test 
whether multinational firms outperform domestic firms that operate in the country in which they 
are headquartered. Section five reports similar regression results using an alternative 
international benchmark; this enables us to test whether multinational firms outperform an 
international portfolio of domestic firms. Section six describes the impact of excluding 
conglomerate firms from the sample, while section seven provides a conclusion. 

Data and Methodology 
Description of Data 

Our primary data source is the Worldscope database. Worldscope has product and 
geographic segment data on more than 8,000 firms, covering 49 countries. The firms in this 



58 

database represent more than 85% of the world's total market capitalization. The Worldscope 
database disaggregates sales along two dimensions: business segments and geographic regions. 
The business segment data breaks down the company's sales according to product markets, and 
the geographic region data breaks down sales according to the country and/or region where the 
products are sold. The business segment data starts in 1991. The geographic region data, 
however, is primarily available for only the most developed markets. Given these constraints, our 
analysis focuses on firms in four developed countries (Germany, Japan, the U.K., and the U.S.), 
over the time period from 1991 to 1995. 

We use a combination of business segment and geographic region data to categorize 
firms (in each of the four countries) into the four categories outlined below: 



Single Region (GEO=0) Multiple Region (GEOl) 



Single Segment (SEG=0) Domestic/Focused Multinational/Focused 

Multiple Segment (SEG=1) Domestic/Conglomerate Multinational/Conglomerate 



Following Lins and Servaes (1999) and Fauver, Houston, and Naranjo (1999), we use two-digit 
SIC codes to classify firms along product lines. In order to be considered geographically 
diversified, the firm must have more than 10 percent of their total sales outside of their home 
country. Furthermore, we remove firms from the sample whose primary activity is financial 
services (firms with more than fifty percent of their total sales allocated to SIC codes 6000-6999) 
because their sales are irregularly disclosed. Finally, private companies are omitted because the 
computation of market capitalization requires market prices. 
Description of Methodology 

We estimate the value of corporate geographic diversification by modifying the 
techniques used by Berger and Ofek (1995). Specifically, we utilize the firm's market capital-to- 
sales ratio as a measure of corporate profitability. The market value of equity plus the book value 
of debt is used as an estimate of the total market value of the firm's capital. Berger and Ofek 
(1995) and Bodnar, Tang, and Weintrop (1998) use the same ratio to determine profitability, but 



59 

they also consider two other ratios: the ratio of total capital (or price)-to-earnings and the ratio of 
total capital-to-assets. In their analysis, the three measures yield similar results. Because of the 
limited reporting of segment assets and earnings for firms outside the United States, we are forced 
to use the total market capital-to-sales ratio as the sole measure of firm value. 

All else equal, if diversification increases firm value, we would expect diversified firms 
to have higher market-to-sales ratios. Lamont and Polk (1999) show that higher market-to-sales 
ratios may arise for two reasons: (1) product and geographic diversification may produce 
valuable operating synergies which lead to higher cash flows and higher market values for a 
given level of sales; (2) diversification may reduce the firm's risk and cost of capital, in which 
case the higher market value stems from a lower discount rate. 

Because of differences in capital intensity, growth opportunities, and other factors, we 
would expect the market-to-sales ratio to vary considerably among firms in different industries. 
Therefore, we need to control for industry effects when estimating the impact that diversification 
has on firm value. To control for industry effects, we calculate the excess value of each firm by 
taking the difference between the firm's actual performance and its imputed performance. Actual 
performance is measured by the consolidated firm's capital-to-sales ratio. For single-segment 
firms, imputed value is calculated as the median market-to-sales ratio among all pure-play 
(single-segment firms) within the same industry and same country. For multi-segment firms, 
imputed value is calculated by taking a weighted-average of the imputed values for each of the 
firm's segments, where the weights reflect the proportion of the overall firm's sales that come 
from each segment. Multi-segment firms have a positive excess value (i.e., a premium) if the 
overall company's value is greater than the "sum of the parts." By contrast, multi-segment firms 
have a negative excess value if their value is less than the imputed value that would be obtained 
by taking a portfolio of pure-play firms that operate in the same industries and country as the 
diversified firm. 



60 

We consider two approaches for estimating imputed performance. The first approach 
(the domestic benchmark) compares the firm's performance to firms that operate in the same 
industry(s) and within the country that the firm is headquartered. This benchmark, which is also 
employed by Bodnar, Tang, and Weintrop (1998) and Denis, Denis, and Yost (1999), indicates 
whether the average multinational firm trades at a premium or discount relative to the domestic 
firms in its home country. In effect, this approach addresses the issue of whether geographic 
diversification increases firm value. 

The second approach (the international benchmark) also compares the firm's 
performance to firms that operate in the same industry(s), but here imputed performance is based 
on a weighted average of the imputed values for the various countries in which the firm operates. 
So, for example, if a computer firm has 70% of its sales in the U.S. and 30% in Canada, the 
imputed value, using the international benchmark would be: 

(.7)(the value of the median pure-play U.S. computer firm) 
+ (.3)(the value of the median pure-play Canadian computer firm). 
Once again, we use the market-to-sales ratio to measure value. This international benchmark 
enables us to examine whether multinational firms trade at a premium relative to a portfolio of 
firms from each of the different countries. In effect, this approach addresses the question of 
whether investors are better off investing in multinational firms or investing in a portfolio of 
domestic firms from different countries. One drawback of using the international benchmark is 
that while we have product market segments and geographic regions, we do not have the 
breakdown among both. So, for example, if a multinational's product segments are 60% 
computers and 40% shoes, we are forced to assume that the multinational has the same product 
mix throughout its various geographic segments, even though this is unlikely to be the case. In 
Section VI of the paper, we address this potential deficiency by excluding conglomerate firms 
from the sample. 



61 

Finally, for each of the two benchmarks, we remove firms where the actual value is more 
than four times the imputed value, or when the imputed value is more than four times the actual 
value. This removal is done to avoid biases associated with unrealistic outliers and is similar to 
the approach used in previous studies. 

Results 

Table 3-1 displays the summary statistics for the firms in our sample broken down by the 
four types of firms: single-industry - domestic and multinational and multi-industry — domestic 
and multinational. This table is also separated into four panels, with Panels A-D providing the 
descriptive statistics for German, Japanese, U.K., and U.S. firms respectively. For each variable, 
the top figure in each panel displays the mean value, below which we provide the median value in 
parentheses. On the right side of each panel, we provide statistical tests for differences in the 
mean and median value for each variable across the four firm types. 

A couple of interesting patterns emerge when we compare the data for the four countries. 
In each of the four countries, the average conglomerate has roughly 2.5 business segments. 
However, the average number of geographic segments varies considerably - Japanese 
multinationals have the fewest geographic segments (1 .6), while German multinationals have the 
most segments (4.0). In each country, domestic firms outnumber multinational firms - the 
percentage of domestic firms ranges from 66% in Germany to 85% in the United States. The 
percentage of single-industry firms ranges from 47% in Japan to 71% in the United States. 

While the average firm characteristics are fairly similar across the four countries, some 
notable differences do emerge. U.S. firms generally have the highest leverage ratios, the highest 
market-to-sales ratios, and the highest profitability (as measured by operating income/sales). 
German firms, on the other hand, tend to have the lowest market-to-sales ratio but the highest 
level of ownership concentration. The Japanese firms in our sample are the largest on average - 
where size is measured in terms of both total assets and total capital. 



62 

It is also interesting to note that for each of the four countries, focused/ multinational 
firms have the highest market-to-sales ratios, and that the second highest ratios are found for 
focused/domestic firms. Moreover, this pattern (not reported) holds for each year of the sample 
period. These unconditional results suggest that focused firms consistently trade at higher 
multiples relative to conglomerates and that among focused firms, multinationals trade at higher 
multiples relative to domestic firms. While these results seem to indicate that geographic 
diversification creates value, and that product market diversification reduces value, it remains 
unclear whether diversification itself affects value, or whether there are other factors affecting 
firm value that are correlated with the level of diversification. 

To get at this issue, we estimate regressions below that control for various firm 
characteristics. As shown in Table 3-1, within each country, there are also some significant 
differences between the focused and conglomerate firms and between the domestic and 
multinational firms. While the exact nature of these differences varies considerably, their 
presence suggests that it is important that our subsequent regression analyses control for these 
firm characteristics when analyzing the effects of industrial and geographical diversification. 
Excess Value Created by Product Market and Geographic Diversification 

Table 3-2 reports the mean and median excess value estimates by country for the four 
types of firms: single-industry - domestic and multinational and multi-industry - domestic and 
multinational. Panel A reports the results using the standard domestic benchmark to calculate 
imputed value, whereas Panel B reports the results using the international benchmark. 

Looking first at Panel A, we see that among domestic firms in each of the four countries, 
focused firms significantly outperform conglomerates (see columns 1 and 3). This result is 
strongly consistent with earlier studies that find a product diversification discount among firms in 
the leading industrialized countries. However, among multinational firms, conglomerates 
perform significantly worse than single-industry firms in Japan and the United States (see 
columns 2 and 4). 



63 

Interestingly, for both Japan and the U.S., single-industry firms that are geographically 
diversified (single-industry, multinational) trade at premiums relative to comparable 
geographically focused firms (single-industry, domestic) (see columns 1 and 2). The results 
indicate a premium of 14.1 percent for Japanese firms and 7.8 percent for U.S. firms. By 
contrast, for Germany, we find that single-industry firms that are geographically diversified 
(multinationals) trade at a discount of 10.5 percent relative to comparable geographically focused 
firms (single-industry, domestic). For single-industry firms in the U.K., there is no significant 
difference between the value of domestic and multinational firms. Finally, geographic 
diversification also appears to have no significant effect on the firm value of multi-industry firms 
in any of the four countries (see columns 3 and 4). Overall, the results in Panel A suggest that 
industry diversification reduces firm value while geographic diversification potentially adds value 
for firms that operate in a single industry. 

Turning our attention to Panel B where we use corresponding international firms as the 
benchmark, we see that once again industrial diversification reduces value for Japanese and U.S. 
firms. However, when we use the international benchmark to calculate imputed value, we find 
that that geographic diversification has a significant effect on excess value only for German 
firms. This result indicates that benchmark considerations are important in the determination and 
interpretation of the value associated with geographic diversification. 

Looking jointly at the results from Panel A and Panel B, it appears that multinationals 
have higher multiples than their domestic counterparts in the same industry, but that the 
multinationals do not generally outperform a portfolio of domestic firms from each of the 
countries where they have operations. 
Do Multinational Firms Outperform their Domestic Counterparts? 

While the results in Table 3-2 provide an overall depiction of the value of geographic and 
product market diversification among the four countries, they do not control for individual firm 
characteristics that are also likely to affect the firm's market-to-sales ratio. These other 



64 

characteristics include the firm's size, profitability, future growth opportunities, and ownership 

structure. As mentioned above, previous studies have found important links between ownership 

concentration and firm value and between ownership concentration and the value of product 

market diversification. However, one drawback of incorporating ownership is that this data is 

available for only a subset of firms in the sample. Consequently, we have chosen to report the 

results both with and without ownership concentration as a control variable. 

Regressions that Omit Ownership Concentration as a Control Variable 

Our first set of regressions includes indicator variables corresponding to product and 

geographic diversification along with firm characteristics, excluding ownership concentration. 

Specifically, we estimate the following regression model for each of the four countries in our 

sample: 

(1) Excess Value - a + /?, (Industry Diversification Dummy) + /?, (Geographic Diversification Dummy) 
+ /? 3 (Relative Log Assets) + /? 4 (Relative Operating Income/ Sales) 
+ ft ^(Relative Capital Expenditures I Sales) + e. 

Excess value is defined to be the natural log of the ratio of the firm's market value to its imputed 

value. The product market diversification dummy (SEG) is equal to one for multi-segment firms 

and is set to zero for focused (single product) firms. The geographic diversification dummy 

(GEO) is equal to one for multinational firms and equals zero for domestic firms. The log of the 

relative assets controls for potential firm size effects. The ratio of operating mcome-to-sales 

(OIS) provides a measure of firm profitability, while the ratio of capital expenditures-to-sales 

(CES) proxies for the level of growth opportunities. Controlling for the other factors, we would 

expect to see a positive link between excess value and both OIS and CES. 4 Since our data covers 

five years (1991-1995), we also include separate year dummies in the regressions to control for 

intertemporal variations in market or economic conditions that may also affect the firm's market- 

to-sales ratio. Lastly, since the dependent variable is measured in relative terms, we also measure 



For Japan, CES is irregularly reported, and we therefore exclude it from the regression 
specification. When we include CES for Japan, we obtain similar results, although the sample is 
much smaller. 



65 

the independent variables in relative terms. In particular, the independent variables are all 
measured relative to the value of the weighted-average multiplier firms that form the basis for the 
excess value measure. 

The regression results for the individual countries (not including ownership) are reported 
in the first four columns of Table 3-3. As expected, we find that the estimated coefficients on 
OIS (Relative Operating Income/Sales) and CES (Relative Capital Expenditures/Sales) are 
positive and frequently significant. These results confirm that firms that are more profitable and 
that have greater growth opportunities typically have higher market-to-sales ratios. The estimated 
coefficient for the log of the relative size variable is significant and negative for firms in Germany 
and Japan, but is significant and positive for firms in the U.K. and U.S. Although not reported, 
the annual dummy coefficients indicate that there is little time variation in the excess values after 
controlling for firm characteristics. 

The estimated coefficients on the product market diversification dummy appear to be 
reasonable and are generally well within the ranges found in earlier studies. Among U.S. firms, 
we find a diversification discount of 18.4 percent, which is similar to the 14.4 percent discount 
found by Berger and Ofek (1995) over an earlier time period 1986-1991. Moreover, our 
estimated diversification discount for U.S. firms is also similar to those reported by Bodnar, 
Tang, and Weintrop (1998) and Denis, Denis, and Yost (1999), who also control for geographic 
diversification. For Japan and the U.K., we find statistically significant product market 
diversification discounts of 7.1 percent and 10.5 percent respectively. These diversification 
discounts are generally similar to those found by Lins and Servaes (1999) and Fauver, Houston, 
and Naranjo (1999), but neither of those studies controlled for the effects of geographic 
diversification. All in all, our results strongly confirm earlier findings and suggest that focused 
firms outperform conglomerate firms in the most developed markets. 

The results also indicate that multinational firms in the United States and Japan are 
valued more highly than their domestic counterparts. Controlling for other factors, Japanese 



66 

multinationals trade at a 7 percent premium - the coefficient on the geographic diversification 
dummy (GEO) is 0.070, which is significant at the one percent level. Likewise, U.S. 
multinationals trade at a 5.5 percent premium - which is significant at the one percent level. This 
result is consistent with the findings of Bodnar, Tang, and Weintrop (1998), who also find that 
U.S. multinationals trade at a significant premium relative to domestic U.S. firms. However, 
Bodnar, Tang, and Weintrop's estimated premium of 2.2 percent is somewhat smaller than the 5.5 
percent premium that we find for the firms in our sample. By contrast, German multinationals 
trade at a discount of 8 percent relative to German domestic firms (this difference is significant at 
the one percent level). Finally, geographic diversification does not appear to have a significant 
effect on firm value in the U.K. 5 
Regression Results Controlling for Ownership Concentration 

The results discussed above suggest that corporate product diversification is costly and 
geographic diversification may be beneficial. A potential problem with this conclusion is that, so 
far, we have not explicitly controlled for agency costs associated with ownership concentration. 
Indeed, several studies suggest that firm value is correlated with ownership structure [e.g., 
Demsetz and Lehn (1985), Morck, Shleifer and Vishny (1988), Holderness and Sheehan (1998), 
and McConnell and Servaes (1990)] and that ownership structure varies across countries and 
legal systems [e.g., La Porta, Lopez-de-Silanes, Shleifer and Vishny (1997, 1998), LaPorta, 
Lopez-De-Silanes and Shleifer (1999), and Claessens, Djankov, Fan and Lang (1998)]. To the 
extent that ownership concentration affects firm value, it may also affect the estimated value of 
corporate diversification. This concern may be particularly relevant if there is a strong link 
between ownership concentration and firm value and if focused and diversified firms have 
significantly different levels of ownership concentration. An additional concern is that even if 
ownership concentration levels are similar for both focused and diversified firms, ownership 



As a robustness check, we also included an interaction indicator variable between the product 
(SEG) and geographic (GEO) diversification dummies as an explanatory variable. In each case, 
the interactive term was insignificantly different from zero. 



67 

concentration may still be important if it has a differential effect on the value of focused and 
diversified firms. 

The exact link between ownership structure and firm value, however, is not entirely clear. 
On one hand, it is widely acknowledged that concentrated ownership is likely to reduce the 
conflicts that arise when there is a separation between managers and stockholders. This link 
suggests a positive relation between firm value and ownership concentration. On the other hand, 
concentrated ownership provides large investors with opportunities to exploit minority 
shareholders, thereby suggesting at least for some range of values a negative relation between 
firm value and ownership concentration. In a recent study, Holderness and Sheehan (1998) 
conclude that in the United States, legal constraints often effectively limit the actions of majority 
shareholders - but it is not clear to what extent their conclusions extend outside the U.S. 

As reported in the summary statistics in Table 3-1, German firms have a significantly 
higher level of average ownership concentration. We also find that within each country, 
ownership concentration levels differ across the four firm types, although no clear patterns 
consistently emerge among the four countries. However, given the clear correlation between 
organizational structure and ownership structure, it is important that we control for ownership 
concentration when estimating the sources of any diversification discounts or premiums. 

Similar to Morck, Shleifer, and Vishny (1988) and others, we account for the nonlinear 
relation between ownership structure and firm value by creating three separate ownership 
concentration variables: 6 

OWNOtolO = total ownership if total ownership < 0.10, 
= 0.10 if total ownership 0.10; 

OWN10to30 =0 if total ownership < 0.10, 



6 Morck, Shleifer and Vishny (MSV, 1988) use 5 percent and 25 percent as their breakpoints. 
Given that the Worldscope databank does not generally provide firm level ownership 
concentration values below 5 percent (aside from the unreported values), we use a 10 percent cut- 
off for the first breakpoint and 30 percent as the next breakpoint to be consistent with MSV's 
ownership ranges. 



68 

= total ownership minus 0.10 if 0. 1 total ownership < 0.30, 

= 0.20 if total ownership 0.30; 
OWNover30 =0 if total ownership < 0.30, 

= total ownership minus 0.30 if total ownership 0.30. 
This classification suggests that the marginal impact of increased ownership concentration vanes 
depending on whether ownership concentration is less than 1 percent, between 1 and 30 
percent, and greater than 30 percent. To assess the impact of ownership concentration on the 
value of corporate diversification, we also interact OWN10to30 and OWNover30 with the 
dummy variables SEG and GEO, which equal one if the firm has multiple industry segments and 
has sales in more than one country. 7 Generally, we would expect a positive link between firm 
value and OWNOtolO. Within this range, increases in ownership concentration are likely to 
improve managerial incentives without dramatically increasing the risks of managerial 
entrenchment and expropriation. For ownership concentration levels beyond ten percent, the 
expected results are less clear. For these firms, the benefits of increased ownership may be more 
than offset by the costs resulting from increased managerial entrenchment and by the potential for 
the expropriation of minority shareholders. Consequently, the link between OWN10to30 and 
OWNover30 and firm value is less clear. 

The firm level regression estimates that control for ownership concentration are reported 
in columns 5-8 of Table 3-3. Looking at the multi-industry dummy coefficients (SEG), we find 
that firms that diversify along product lines in Japan, the U.S., and the U.K. continue to trade at a 
discount relative to focused firms. Moreover, the magnitude of the estimated coefficient 
increases in Japan and slightly decreases m the U.S. after controlling for ownership concentration. 
With the augmented specification, we still find that German multi-industry firms do no worse (or 
better) relative to focused firms. 



7 Note that due to singularity, we do not include OWN0tol0*SEG in our specification. 



69 

Controlling for ownership concentration also has an effect on the estimated coefficients 
for the multi-country segment dummy (GEO). Specifically, the estimated coefficients for 
Japanese and U.S. firms are higher after controlling for ownership - once again confirming that 
Japanese and U.S. multinationals are valued more highly than their domestic counterparts. The 
geographic diversification coefficient for Germany is now insignificant after controlling for 
ownership. Finally, the estimated geographic diversification coefficient for the U.K. firms 
remains insignificant. 

While it is not the primary focus of our analysis, the estimated coefficients for the 
ownership concentration are still of considerable interest. First, for low levels of ownership 
concentration, there is a positive link between ownership concentration and excess value for U.S. 
firms, while the relation is insignificant for firms in the other three countries. Second, for 
ownership concentration levels beyond ten percent, we generally find that increases in ownership 
concentration lead to a reduction in value for both focused and diversified firms. This result 
confirms the fact that there are both costs and benefits associated with increased ownership 
concentration. Finally, from the coefficients on the ownership concentration variables that are 
interacted with the diversification dummy (OWN10to30*SEG, OWNover30*SEG, 
OWN10to30*GEO, and OWNover30*GEO), we see that the effects of ownership concentration 
are significantly different for focused and diversified firms. For ownership concentration levels 
between 10 and 30 percent, excess value is significantly lower for the diversified firms, 
suggesting that entrenchment problems and expropriation of minority shareholders is more of a 
concern for diversified firms. However, beyond 30 percent, excess value is significantly higher 
for diversified firms. For example, a geographically diversified firm in the United States with a 
concentrated ownership of 35 percent would be valued 8 percent more (0.232*0.35) relative to a 
domestic focused firm with concentrated ownership below 10 percent. All in all, the results 
suggest that there is a link between ownership concentration and excess value, and that this link 
may be somewhat different for focused and diversified firms. 



70 

Do Multinationals Outperform a Portfolio of Domestic Firms From Different Countries? 

The results in the previous section indicate that U.S. and Japanese multinationals are 
valued, on average, more highly than their domestic counterparts. While these results suggest 
that geographic diversification enhances firm value in these countries, it is unclear whether 
multinationals outperform a portfolio of domestic firms in the various countries in which they 
operate. To get at this issue, we compared multinational firms to a weighted average of firms that 
have the same geographic and product mix. These results are reported in Table 3-4. Once again, 
the regression results excluding ownership are reported in columns 1-4, while the results 
including ownership concentration are reported in columns 5-8. 

After controlling for ownership concentration, we find that the coefficient on the 
geographic diversification dummy (GEO) is not significantly different from zero in each of the 
four countries. These results suggest that while multinational firms in the U.S. and Japan 
outperform their domestic counterparts, multinational firms do not outperform a simulated 
portfolio of international firms that mimic their overall product mix. Interestingly, these results 
parallel some recent findings in the international investments literature. Heston and Rouwenhorst 
(1994) and Griffin and Karolyi (1998), for example, suggest that while multinational firms have 
higher risk-adjusted returns, shareholders can duplicate these same risk-adjusted returns by 
holding a portfolio of domestic firms in each international market. Moreover, similar to the 
above studies, we also find that the value of geographic diversification largely arises from 
differences in performance across countries, not from differences in industry composition or 
clustering across countries. Interestingly, these results parallel Heston and Rouwenhorst ( 1 994) 
and Griffin and Karolyi (1998) who use international indice return data, whereas we use firm 
level corporate data in our analysis. In another recent investments study that also parallels our 
results, Rowland and Tesar (1998) examine the return mean-variance efficient frontier with 
domestic firms, multinational firms, and international equity indices. They first find that 
multinational firms add risk-adjusted value to a portfolio of domestic firms. However, when the 



71 

domestic portfolio is augmented with international indices, the multinational firms do not add any 
additional value. 

In a related stream of the international investments literature that also parallels our 
findings, several studies find that investors overweight their portfolios with domestic securities 
relative to international securities. In particular, French and Poterba (1991) and Cooper and 
Kaplanis (1994) among others find that there is a "home bias" towards investment in domestic 
securities. Given that investors are reluctant to purchase overseas investments, domestically 
headquartered multinational firms often serve as a method to obtain some international exposure, 
resulting in a potentially higher valuation of multinational firms. Therefore, one might expect 
that countries with relatively greater home bias would likely value multinational firms more than 
in countries where there is less home bias, all else equal. Given that French and Poterba (1991) 
find that domestic portfolio dedication (home bias) is greatest in Japan and the U.S. for the 
countries in our sample, we might expect that multinationals in these countries would be valued 
more highly. Consistent with this conjecture, we find that multinationals in Japan and the U.S. 
trade a premium when using the domestic benchmark. However, when using the international 
benchmark, the valuation premiums disappear. 
Taking a Closer Look at the Single-Segment Firms 

As a final test of the value of geographic diversification, we re-estimate the results for the 
sub-sample of focused firms that have sales in a single segment. There are two benefits that arise 
from eliminating the conglomerate firms from the sample. First, as we have seen, there may be 
important interaction effects between the value of product-market diversification and the value of 
geographic diversification. In this regard, eliminating the conglomerate firms removes the effects 
of product-market diversification, thereby potentially providing us with a cleaner test of the value 
of geographic diversification. Second, we indicated above that one problem with the 
international benchmark results reported in Table 3-4 is that data limitations forced us to assume 
that multinationals have the same product mix in each of their geographic regions segments, even 



72 

though this was unlikely to be the case. Once again, this concern disappears if we eliminate 
conglomerate firms from the sample. 

The regression results for the sub-sample of single-segment firms are reported in Table 3- 
5. These regressions include ownership concentration as a control variable, and are estimated 
using both the domestic and international benchmarks for computing imputed value. These 
results directly parallel the domestic and international benchmark results reported earlier in 
Tables 3-3 and 3-4. In Table 3-5, Panel A, with the domestic benchmark, we find a geographic 
diversification premium in the U.S. and Japan and a geographic diversification discount in 
Germany. Using the international benchmark, Panel B, we find that the geographic 
diversification premiums and discount disappear similar to Table 3-4. Once again, this confirms 
the conclusion that for U.S. and Japanese firms, geographic diversification increases value, but 
that multinationals do not trade at a premium relative to an international portfolio of domestic 
firms. 

Conclusion 

While in recent years a large literature has examined the links between product 
diversification and firm value, considerably fewer studies have examined the value of geographic 
diversification. The lack of work in this area is surprising given the dramatic growth in foreign 
investment among firms in the leading industrialized countries over the past twenty-five years. 

In this paper, we investigate the connection between product and geographic 
diversification and its impact on firm value. We gather data on more than 4,000 firms from four 
highly industrialized countries (Germany, Japan, the U.K., and the U.S.). On average, we find 
that the geographic diversification neither enhances nor reduces the value of multinationals 
located in Germany and the United Kingdom. However, our results suggest that geographic 
diversification does significantly enhance the value of multinational firms in Japan and the United 
States. These results suggest that multinationals in these countries are able to capture valuable 
operating synergies or generate benefits from risk reduction. However, we also find that in all 



73 



four countries, multinationals typically do not outperform an international portfolio of domestic 
firms. 

Interestingly, our results parallel some recent findings in the international investments 
literature. Recent studies by Heston and Rouwenhorst (1994), Griffin and Karolyi (1998), and 
Rowland and Tesar (1998), for instance, find that while multinational firms have higher risk- 
adjusted returns, shareholders can duplicate these same risk-adjusted returns by holding a 
portfolio of domestic firms in each international market. Overall, our results suggest that there 
are important interactions between the value of product market diversification and geographic 
diversification and that future studies need to consider both forms of diversification when 
investigating the links between diversification and firm value. 









74 



Table 3-1 
Summary Statistics by Industrial and Geographical Diversification: 1991 - 1995 

Panel A: German Firms 



Firm Level Characteristics by 
Industrial and Geographical 
Diversification 


Single-Industry Firms 


Multi-Industry Firms 


Domestic 
(1) 


Multinational 
(2) 


Domestic 
(3) 


Multinational 
(4) 


Number of Industrial 
Segments 


1 
(1) 


1 
(1) 


2.713 
(2) 


2.550 
(2) 


Number of Geographical 
Segments 


1 
(1) 


4.037 
(4) 


1 
(1) 


3.759 
(3) 


Total Assets (mil $) 


1,550 
(220) 


564 
(153) 


4,420 
(683) 


1,400 
(365) 


Total Capital (mil $) 


531 
(89) 


259 

(57) 


1,460 
(273) 


450 
(147) 


Leverage Ratio 


0.193 
(0.168) 


0.203 
(0.181) 


0.185 

(0.144) 


0.227 
(0.157) 


Operating Income/Sales 


0.053 
(0.059) 


0.048 
(0.061) 


0.044 
(0.046) 


0.043 
(0.046) 


Capital Expenditure/Sales 


0.082 
(0.055) 


0.078 
(0.056) 


0.075 
(0.055) 


0.065 

(0.050) 


Ownership Concentration 


0.612 
(0.650) 


0.619 
(0.700) 


0.505 
(0.514) 


0.574 
(0.660) 


Market/Sales 


0.785 
(0.597) 


0.842 
(0.557) 


0.656 
(0.463) 


0.702 
(0.540) 


Observations 


538 


272 


366 


191 






75 





Panel A— continued 








Firm Level Characteristics by 
Industrial and Geographical 
Diversification 


Test of Statistical Differences 
p-values 


(D-(2) 


(D-(3) 


(D-(4) 


(2)-(3) 


(2)-(4) 


(3)-(4) 


Number of Industrial 
Segments 














Number of Geographical 
Segments 














Total Assets (mil $) 


0.000 
(0.001) 


0.000 
(0.000) 


0.707 
(0.000) 


0.000 
(0.000) 


0.008 
(0.000) 


0.000 
(0.000) 


Total Capital (mil $) 


0.002 
(0.005) 


0.000 
(0.000) 


0.534 
(0.001) 


0.000 
(0.000) 


0.087 
(0.000) 


0.000 
(0.001) 


Leverage Ratio 


0.412 
(0.452) 


0.407 
(0.809) 


0.093 
(0.229) 


0.143 
(0.346) 


0.263 
(0.592) 


0.038 
(0.224) 


Operating Income/Sales 


0.641 

(0.882) 


0.120 
(0.021) 


0.182 
(0.081) 


0.732 
(0.078) 


0.699 

(0.080) 


0.908 
(0.766) 


Capital Expenditure/Sales 


0.601 
(0.766) 


0.313 
(1.000) 


0.005 
(0.159) 


0.719 
(0.873) 


0.080 
(0.080) 


0.131 
(0.028) 


Ownership Concentration 


0.761 
(0.040) 


0.000 
(0.004) 


0.131 
(0.773) 


0.000 
(0.001) 


0.122 
(0.093) 


0.013 
(0.018) 


Market/Sales 


0.529 
(0.457) 


0.000 
(0.000) 


0.157 
(0.215) 


0.035 
(0.006) 


0.139 

(0.807) 


0.412 
(0.024) 


Observations 















76 



Panel B: Japanese Firms 



Firm Level Characteristics by 
Industrial and Geographical 
Diversification 


Single-Industry Firms 


Multi-Industry Firms 


Domestic 
(1) 


Multinational 
(2) 


Domestic 
(3) 


Multinational 
(4) 


Number of Industrial 
Segments 


1 
(1) 


1 
(1) 


2.536 
(2) 


2.569 
(2) 


Number of Geographical 
Segments 


1 
(1) 


1.689 

(2) 


1 
(1) 


1.658 
(2) 


Total Assets (mil $) 


3,010 
(406) 


2,070 
(297) 


3,450 
(401) 


1,230 
(299) 


Total Capital (mil $) 


1,850 
(214) 


992 
(195) 


1,540 
(202) 


530 
(146) 


Leverage Ratio 


0.265 
(0.242) 


0.241 
(0.216) 


0.282 
(0.270) 


0.264 
(0.250) 


Operating Income/Sales 


0.084 
(0.076) 


0.078 
(0.073) 


0.075 
(0.067) 


0.079 
(0.069) 


Capital Expenditure/Sales 










Ownership Concentration 


0.266 

(0.226) 


0.251 
(0.214) 


0.267 
(0.236) 


0.293 
(0.252) 


Market/Sales 


1.176 
(0.944) 


1.241 
(1.063) 


1.054 
(0.841) 


1.034 
(0.857) 


Observations 


1,585 


225 


1,802 


260 






77 



Panel B— continued 



Firm Level Characteristics by 
Industrial and Geographical 
Diversification 


Test of Statistical Differences 
p-values 


(D-(2) 


d)-(3) 


(l)-(4) 


(2)-(3) 


(2)-(4) 


(3)-(4) 


Number of Industrial 
Segments 














Number of Geographical 
Segments 














Total Assets (mil $) 


0.028 
(0.005) 


0.231 
(0.851) 


0.000 
(0.062) 


0.001 
(0.006) 


0.039 

(0.818) 


0.000 
(0.063) 


Total Capital (mil $) 


0.000 
(0.354) 


0.144 
(0.547) 


0.000 
(0.016) 


0.001 
(0.530) 


0.003 
(0.221) 


0.000 
(0.024) 


Leverage Ratio 


0.060 
(0.078) 


0.014 
(0.007) 


0.930 

(0.875) 


0.002 
(0.001) 


0.164 
(0.105) 


0.159 
(0.201) 


Operating Income/Sales 


0.215 
(0.354) 


0.000 
(0.000) 


0.217 
(0.084) 


0.466 
(0.040) 


0.970 
(0.297) 


0.419 
(0.596) 


Capital Expenditure/Sales 














Ownership Concentration 


0.260 

(0.571) 


0.881 
(0.521) 


0.038 
(0.136) 


0.228 
(0.567) 


0.015 
(0.270) 


0.045 
(0.422) 


Market/Sales 


0.268 
(0.075) 


0.000 
(0.000) 


0.002 
(0.144) 


0.001 
(0.002) 


0.003 
(0.020) 


0.670 
(0.791) 


Observations 















78 



Panel C: U.K. Firms 



Firm Level Characteristics by 
Industrial and Geographical 
Diversification 


Single-Industry Firms 


Multi-Industry Firms 


Domestic 
(1) 


Multinational 
(2) 


Domestic 
(3) 


Multinational 
(4) 


Number of Industrial 
Segments 


1 
(1) 


1 
(1) 


2.657 
(2) 


2.553 
(2) 


Number of Geographical 
Segments 


1 
(1) 


3.651 
(4) 


1 
(1) 


3.790 
(4) 


Total Assets (mil $) 


847 
(68) 


579 
(54) 


1,480 
(169) 


556 
(90) 


Total Capital (mil S) 


514 
(34) 


385 
(27) 


844 
(90) 


337 
(54) 


Leverage Ratio 


0.198 
(0.165) 


0.208 
(0.167) 


0.222 
(0.199) 


0.214 
(0.184) 


Operating Income/Sales 


0.121 
(0.101) 


0.117 
(0.101) 


0.102 
(0.095) 


0.112 
(0.101) 


Capital Expenditure/Sales 


0.084 
(0.039) 


0.109 
(0.035) 


0.057 
(0.038) 


0.072 
(0.037) 


Ownership Concentration 


0.359 
(0.351) 


0.397 
(0.377) 


0.266 
(0.239) 


0.303 
(0.294) 


Market/Sales 


1.151 
(0.800) 


1.235 
(0.804) 


0.936 

(0.749) 


1.137 
(0.751) 


Observations 


2,048 


789 


1,412 


476 









79 



Panel C—continued 



Firm Level Characteristics by 
Industrial and Geographical 
Diversification 


Test of Statistical Differences 
p-values 


(D-(2) 


(l)-(3) 


(D-(4) 


(2)-(3) 


(2)-(4) 


(3)-(4) 


Number of Industrial 
Segments 














Number of Geographical 
Segments 














Total Assets (mil $) 


0.063 
(0.009) 


0.000 
(0.000) 


0.029 
(0.003) 


0.000 
(0.000) 


0.883 
(0.000) 


0.000 
(0.000) 


Total Capital (mil $) 


0.184 
(0.001) 


0.000 
(0.000) 


0.050 
(0.000) 


0.000 
(0.000) 


0.674 
(0.000) 


0.000 
(0.000) 


Leverage Ratio 


0.317 
(0.395) 


0.014 
(0.000) 


0.186 
(0.004) 


0.241 
(0.000) 


0.676 
(0.067) 


0.543 
(0.118) 


Operating Income/Sales 


0.497 
(0.976) 


0.000 
(0.009) 


0.235 
(0.919) 


0.004 
(0.192) 


0.512 
(0.890) 


0.237 
(0.138) 


Capital Expenditure/Sales 


0.007 
(0.124) 


0.000 
(0.368) 


0.061 
(0.309) 


0.000 
(0.203) 


0.000 
(0.342) 


0.005 
(0.525) 


Ownership Concentration 


0.000 
(0.029) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.003 
(0.000) 


Market/Sales 


0.117 
(0.957) 


0.000 
(0.019) 


0.813 
(0.019) 


0.000 
(0.054) 


0.168 
(0.051) 


0.001 
(0.915) 


Observations 


















80 





Panel D: 1 


U.S. Firms 






Firm Level Characteristics by 
Industrial and Geographical 
Diversification 


Single-Industry Firms 


Multi-Industry Firms 


Domestic 
(1) 


Multinational 
(2) 


Domestic 
(3) 


Multinational 
(4) 


Number of Industrial 
Segments 


1 
(1) 


1 
(1) 


2.428 
(2) 


2.373 
(2) 


Number of Geographical 
Segments 


1 
(1) 


2.668 
(3) 


1 
(1) 


2.688 
(3) 


Total Assets (mil $) 


1,470 
(247) 


807 
(163) 


3,350 
(559) 


971 
(265) 


Total Capital (mil $) 


909 
(171) 


531 
(120) 


1,740 
(358) 


616 
(180) 


Leverage Ratio 


0.253 
(0.234) 


0.247 
(0.207) 


0.278 
(0.260) 


0.279 
(0.276) 


Operating Income/Sales 


0.144 
(0.132) 


0.157 
(0.140) 


0.134 
(0.125) 


0.131 
(0.123) 


Capital Expenditure/Sales 


0.091 
(0.049) 


0.107 
(0.049) 


0.077 
(0.045) 


0.084 
(0.046) 


Ownership Concentration 


0.283 
(0.248) 


0.295 
(0.271) 


0.241 
(0.189) 


0.285 
(0.245) 


Market/Sales 


1.730 
(1.230) 


1.980 
(1.305) 


1.343 
(0.994) 


1.387 
(0.954) 


Observations 


6,891 


1,313 


2,840 


426 



81 



Panel D--continued 



Firm Level Characteristics by 
Industrial and Geographical 
Diversification 


Test of Statistical Differences 
p- values 


(l)-(2) 


(l)-(3) 


(D-(4) 


(2)-(3) 


(2X4) 


(3)-(4) 


Number of Industrial 
Segments 














Number of Geographical 
Segments 














Total Assets (mil S) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.616) 


0.000 
(0.000) 


0.201 

(0.000) 


0.000 
(0.000) 


Total Capital (mil $) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.840) 


0.000 
(0.000) 


0.280 
(0.001) 


0.000 
(0.000) 


Leverage Ratio 


0.398 
(0.026) 


0.000 
(0.000) 


0.011 
(0.002) 


0.000 
(0.000) 


0.006 
(0.000) 


0.892 
(0.642) 


Operating Income/Sales 


0.009 
(0.028) 


0.021 
(0.002) 


0.039 

(0.073) 


0.000 
(0.000) 


0.000 
(0.005) 


0.631 
(0.603) 


Capital Expenditure/Sales 


0.008 
(0.833) 


0.000 
(0.006) 


0.258 
(0.058) 


0.000 
(0.114) 


0.005 
(0.097) 


0.238 
(0.917) 


Ownership Concentration 


0.127 
(0.034) 


0.000 
(0.000) 


0.852 
(0.742) 


0.000 
(0.000) 


0.516 

(0.128) 


0.002 
(0.001) 


Market/Sales 


0.000 
(0.024) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.478 
(0.467) 


Observations 














Panels A-D provide firm level de 


scnntive si 


atistics for 


German. J 


ananese. I J 


.K.. and II 


S firms 



respectively. The upper number in each cell reports the mean value for each variable, while the 
lower number in parentheses reports the median value for each variable. T-tests are used to test 
for differences in each respective mean value, while Wilcoxon rank-sum tests are used to test for 
differences in the median values. Single-industry firms are firms that operate in only one two- 
digit SIC code industry, while multi-industry firms are defined as firms that operate in two or 
more two-digit SIC code industries and no firm segment sales exceed 90% of total firm sales. 
Domestic firms are defined as firms that have over 90% of their total firm sales in their home 
market, while multinational firms are defined as firms that have more than 10% of their total sales 
outside their home market. The leverage ratio is defined as book value of debt divided by total 
assets. Ownership concentration is defined as the sum of individual and/or institutional 
ownership holdings that are equal to or exceed five percent of a firm's common stock. Due to 
missing ownership concentration data, the number of observations is slightly less than that for the 
other reported variables. Market-to-sales is defined as the ratio of a firm's market value of equity 
plus book value of debt to its total sales. 



82 



Table 3-2 
Excess Values by Industrial and Geographical Diversification: 1991 



1995 



Panel A: Domestic Benchmark 



Excess Value by Country 


Single-Industry Firms 


Multi-Industry Firms 


Domestic 

(1) 


Multinational 
(2) 


Domestic 

(3) 


Multinational 
(4) 


German 


0.027 
(0.000) 


-0.078 
(-0.070) 


-0.057 
(-0.078) 


-0.020 
(-0.007) 


Japanese 


0.000 
(0.000) 


0.141 
(0.118) 


-0.051 

(-0.074) 


-0.038 
(0.018) 


U.K. 


0.001 
(0.000) 


-0.009 
(-0.027) 


-0.085 
(-0.093) 


-0.055 
(-0.086) 


U.S. 


-0.020 
(-0.014) 


0.058 
(0.060) 


-0.172 
(-0.174) 


-0.151 
(-0.200) 



Panel A~continued 



Excess Value by Country 


Test of Statistical Differences 
p-values 


(D-(2) 


(D-(3) 


(l)-(4) 


(2)-(3) 


(2)-(4) 


(3X4) 


German 


0.008 
(0.011) 


0.019 
(0.003) 


0.324 
(0.816) 


0.631 
(0.749) 


0.292 
(0.146) 


0.484 
(0.122) 


Japanese 


0.000 
(0.003) 


0.007 
(0.000) 


0.314 
(0.781) 


0.000 
(0.000) 


0.001 
(0.160) 


0.738 
(0.289) 


U.K. 


0.712 
(0.229) 


0.000 
(0.000) 


0.059 
(0.084) 


0.006 

(0.044) 


0.181 
(0.365) 


0.333 
(0.672) 


U.S. 


0.000 
(0.003) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.492 
(0.592) 



83 



Panel B: International Benchmark 



Excess Value by Country 


Single-Industry Firms 


Multi-Industry Firms 


Domestic 
(1) 


Multinational 
(2) 


Domestic 
(3) 


Multinational 
(4) 


German 


0.024 
(0.000) 


-0.017 
(0.044) 


0.028 
(0.045) 


0.282 
(0.244) 


Japanese 


-0.127 
(-0.133) 


-0.105 
(-0.213) 


-0.173 
(-0.176) 


-0.031 
(0.021) 


U.K. 


-0.002 
(0.000) 


-0.123 
(-0.135) 


0.020 
(0.028) 


-0.107 
(-0.164) 


U.S. 


-0.028 
(-0.024) 


-0.038 
(-0.047) 


-0.060 
(-0.060) 


-0.007 
(-0.074) 



Panel B— continued 



Excess Value by Country 


Test of Statistical Differences 
p-values 


0)-(2) 


(D-(3) 


(l)-(4) 


(2X3) 


(2)-(4) 


(3)-(4) 


German 


0.583 
(0.901) 


0.943 
(0.956) 


0.007 
(0.005) 


0.592 
(0.827) 


0.010 
(0.010) 


0.011 
(0.027) 


Japanese 


0.748 
(0.809) 


0.063 
(0.052) 


0.273 
(0.237) 


0.340 
(0.383) 


0.504 
(0.471) 


0.112 
(0.107) 


U.K. 


0.015 
(0.014) 


0.454 
(0.317) 


0.082 
(0.080) 


0.009 
(0.007) 


0.833 
(0.841) 


0.048 
(0.044) 


U.S. 


0.790 
(0.809) 


0.096 
(0.140) 


0.817 
(0.767) 


0.612 
(0.654) 


0.750 
(0.755) 


0.568 
(0.566) 


3 anel A contains the excess valu 


;s usine fh< 


s domestic 


firm bench 


marks, wh 


le Panel B 


contains 



the excess values from using the corresponding international firm benchmarks. With the 
international benchmark, the firm's imputed performance is based on a weighted average of the 
imputed values for the various countries in which the firm operates. The upper number in each 
cell reports the mean value for each variable, while the lower number in parentheses reports the 
median value for each variable. T-tests are used to test for differences in each respective mean 
value, while Wilcoxon rank-sum tests are used to test for differences in the median values. 
Excess value is defined as the natural logarithm of the ratio of a firm's market-to-sales ratio to its 
imputed market-to-sales ratio. Firms with excess values that are greater than four or less than 
one-fourth are eliminated from the sample. Single-industry firms are firms that operate in only 
one two-digit SIC code industry, while multi-industry firms are defined as firms that operate m 
two or more two-digit SIC code industries and no firm segment sales exceed 90% of total firm 
sales. Domestic firms are defined as firms that have over 90% of their total firm sales in their 
home market, while multinational firms are defined as firms that have more than 10% of their 
total sales outside their home market. 



84 



Table 3-3 
Multivariate Regression Estimates of Excess Values Using the Domestic Benchmark: 

1991-1995 



Variables 


German 
(1) 


Japanese 
(2) 


U.K. 
(3) 


U.S. 
(4) 


Constant 


0.096*** 
(-2.59) 


A ITT*** 

(5.64) 


-0.344*** 
(-9.37) 


-0.236*** 
(-13.20) 


Multi-Industry Segment 
Dummy (SEG) 


0.000 
(-0.01) 


-0.071*** 
(-4.07) 


-0.105*** 
(-6.11) 


-0.184*** 
(-15.28) 


Multi-Country Segment 
Dummy (GEO) 


-0.080*** 
(-2.58) 


0.070*** 
(2.58) 


0.019 
(1.03) 


0.055*** 
(3.49) 


Relative Operating 
Income-to-Sales (OIS) 


0.030*** 
(4.56) 


0.024*** 
(2.67) 


0.200*** 
(6.34) 


0.020** 
(2.93) 


Relative Capital 

Expenditures-to-Sales 

(CES) 


0.052*** 
(4.85) 




0.006 
(1.19) 


0.082*** 
(10.44) 




Relative Total Assets 
(ASSETS) 


0.112*** 
(-5.71) 


-0.065*** 
(-5.97) 


0.048*** 
(3.86) 


0.037*** 
(4.29) 


Adjusted R 2 


0.09 


0.06 


0.15 


0.08 


Number of Observations 


1,367 


3,872 


4,725 


11,470 






85 





Table 3-3~continued 






Variables 


German 

(5) 


Japanese 
(6) 


U.K. 

(7) 


U.S. 
(8) 


Constant 


-0.095 
(-1.38) 


0.176*** 
(5.06) 


-0.225*** 
(-4.78) 


-0 194*** 
(-8.24) 


Multi-Industry Segment 
Dummy (SEG) 


-0.033 
(-0.42) 


-0.168*** 
(-6.21) 


-0.076** 
(-2.34) 


-0.170*** 
(-8.25) 


Multi-Country Segment 
Dummy (GEO) 


-0.130 
(-1.52) 


0.116*** 
(2.73) 


0.018 
(0.43) 


0.122*** 
(4.28) 


Relative Operating 
Income-to-Sales (OIS) 


0.032** 
(4.71) 


0.025*** 
(2.63) 


0.201*** 
(5.45) 


0.022*** 
(2.80) 


Relative Capital 

Expenditures-to-Sales 

(CES) 


0.046** 
(4.71) 




0.011* 
(1.88) 


0.074*** 
(8.95) 




Relative Total Assets 
(ASSETS) 


0.118** 
(-5.85) 


-0.067*** 
(-5.73) 


0.004 
(0.29) 


0.024** 
(2.39) 


Ownership Concentration 
< lO(OWNOtolO) 


-0.457 
(-0.46) 


0.234 
(0.68) 


-0.168 
(-0.46) 


0.393* 
(1.80) 


Ownership Concentration 
10-30 (OWN10to30) 


0.851 
(1.35) 


-0.194 
(-0.93) 


-0.389* 
(-1.83) 


-0 419*** 
(-3.06) 


Ownership Concentration 
> 30 (OWNover30) 


0.240** 
(-2.37) 


-0.631*** 
(-3.45) 


-0.230** 
(-2.15) 


-0.083 

(-1.22) 


Ownership Concentration 
10-30 interacted with 
SEG(OWN10to30*SEG) 


-1.004* 
(-1.86) 


0.651** 
(2.41) 


-0.390 
(-1.49) 


-0.220 
(-1.20) 


Ownership Concentration 
> 30 interacted with SEG 
(OWNover30*SEG) 


0.673*** 
(4.14) 


0.453* 
(1.80) 


-0.104 
(-0.63) 


0.123 
(1.08) 


Ownership Concentration 
10-30 interacted with 
GEO 
(OWN10to30*GEO) 


0.277 
(0.46) 


-0.463 
(-1-12) 


-0.161 
(-0.52) 


-0.609** 
(-2.54) 


Ownership Concentration 
> 30 interacted with GEO 
(OWNover30*GEO) 


-0.056 
(-0.32) 


-0.034 
(-0.09) 


0.204 
(1.15) 


0.232* 
(1.66) 


Adjusted R 2 


0.11 


0.07 


0.17 


0.08 


Number of Observations 


1,255 


3,817 


3,823 


8,803 


Significant at 1 percent (*** 


, 5 percent (**),< 


md 10 percent (* 


I levels, Robust- V 


/hite t-statistics 



in parentheses. 



Regression estimates are from 1991-1995. Excess value is defined as the natural logarithm of the 
ratio of a firm's market-to-sales ratio to its imputed market-to-sales ratio. With the domestic 
benchmark, the firm's imputed performance is based on a weighted average of the corresponding 
pure plays within the domestic market. Firms with excess values that are greater than four or less 
than one-fourth are eliminated from the sample. The industry diversification dummy, SEG, is 
equal to one for firms who operate in more than one industry and zero otherwise. Multi-industry 
firms are defined as firms that operate in two or more two-digit SIC code industries and no firm 
segment sales exceed 90% of total firm sales. The multinational diversification dummy, GEO, is 
equal to one for firms who operate in more than one country and zero otherwise. Multinational 
firms are defined as firms that operate in two or more countries and no firm segment sales in a 
particular country exceed 90% of total firm sales. OIS is defined as the firm's operating income- 



86 



to-sales, while CES is the firm's capital expenditures-to-sales. For Japan, we omit the CES 
variable from the specification due to infrequently reported figures. Assets are defined as the 
natural logarithm of the firm's total assets. The independent variables OIS, CES, and ASSETS 
are all measured relative to the value of the weighted-average multiplier firms that form the basis 
for the excess value measure. Ownership concentration is defined as the sum of individual and/or 
institutional ownership holdings that are equal to or exceed five percent of a firm's common 
stock. OWNOtolO: = total ownership if total ownership < 0.10, = 0.10 if total ownership > 
0.10; OWN10to30: = if total ownership < 0.10, = total ownership minus 0.10 if 0.10 < total 
ownership < 0.30, = 0.20 if total ownership > 0.30; OWNover30: = if total ownership < 0.30, 
= total ownership minus 0.30 if total ownership > 0.30. Each model specification also includes 
year dummies for 1992-1995. 



87 



Table 3-4 
Multivariate Regression Estimates of Excess Values Using the International Benchmark: 

1991 - 1995 



Variables 


German 
(1) 


Japanese 
(2) 


U.K. 
(3) 


U.S. 
(4) 


Constant 


-0.071 
(-1-52) 


-0.160*** 

(-3.75) 


-0.421*** 
(-7.64) 


-0.259*** 
(-12.29) 


Multi-Industry Segment 
Dummy (SEG) 


0.052 
(1.15) 


-0.047** 
(-2.09) 


-0.009 
(-0.34) 


-0.048*** 
(-2.71) 


Multi-Country Segment 
Dummy (GEO) 


0.033 
(0.54) 


0.051 
(1.04) 


-0.124*** 
(-3.59) 


-0.015 
(-0.44) 


Relative Operating 
Income-to-Sales (OIS) 


0.013* 
(1.72) 


0.219*** 
(6.38) 


0.283*** 
(6.67) 


0.017** 
(2.29) 


Relative Capital 

Expenditures-to-Sales 

(CES) 


0.046*** 
(2.98) 




0.010 
(0.72) 


0.098*** 
(10.25) 




Relative Total Assets 
(ASSETS) 


-0.081*** 
(-3.27) 


-0.052*** 
(-4.11) 


0.054*** 
(3.33) 


0.035*** 
(3.32) 


Adjusted R 2 


0.07 


0.12 


0.22 


0.08 


Number of Observations 


778 


2,509 


2,739 


7,901 





Table 3-4— continued 






Variables 


German 
(5) 


Japanese 
(6) 


U.K. 
(7) 


U.S. 
(8) 


Constant 


-0.074 
(-0.89) 


-0.126** 
(-2.14) 


-0.321*** 
(-4.61) 


-0.210*** 
(-7.61) 


Multi-Industry Segment 
Dummy (SEG) 


-0.047 
(-0.40) 


-0.124*** 
(-3.48) 


-0.035 
(-0.76) 


-0.028 
(-0.94) 


Multi-Country Segment 
Dummy (GEO) 


-0.106 

(-1.28) 


0.040 
(0.45) 


0.006 
(0.08) 


0.053 
(1.01) 


Relative Operating 
Income-to-Sales (OIS) 


0.017* 
(1.82) 


0.248*** 
(6.62) 


0.278*** 
(5.51) 


0.013* 
(1.82) 


Relative Capital 

Expenditures-to-Sales 

(CES) 


0.043*** 
(2.85) 




0.030*** 
(3.54) 


0.097*** 
(8.63) 




Relative Total Assets 
(ASSETS) 


-0.097*** 
(-3.69) 


-0.057*** 
(-4.12) 


0.010 
(0.52) 


0.023* 
(1.92) 


Ownership Concentration 
< lO(OWNOtolO) 


-0.023 
(-0.02) 


0.111 
(0.26) 


-0.267 
(-0.61) 


0.459* 
(1.74) 


Ownership Concentration 
10-30 (OWN10to30) 


0.358 
(0.48) 


-0.276 
(-1.22) 


-0.350 
(-1.50) 


-0.482*** 
(-3.14) 


Ownership Concentration 
> 30 (OWNover30) 


-0.171 
(-1.62) 


-0.447** 
(-2.54) 


-0.208 

(-1.72) 


-0.040 
(-0.55) 


Ownership Concentration 
10-30 interacted with SEG 
(OWN10to30*SEG) 


-0.522 
(-0.63) 


0.519 

(1.51) 


-0.308 
(-0.80) 


-0.337 

(-1.23) 


Ownership Concentration 
> 30 interacted with SEG 
(OWNover30*SEG) 


0.672*** 
(2.72) 


0.241 
(0.76) 


0.298 
(1.03) 


0.352** 
(2.19) 


Ownership Concentration 
10-30 interacted with GEO 
(OWN10to30*GEO) 


0.132 
(0.13) 


0.410 
(0.45) 


-0.638 
(-1.12) 


-0.537 

(-1.14) 


Ownership Concentration 
> 30 interacted with GEO 
(OWNover30*GEO) 


0.104 
(0.31) 


-0.387 
(-0.53) 


-0.078 
(-0.26) 


0.136 

(0.48) 


Adjusted R 2 


0.09 


0.14 


0.24 


0.08 


Number of Observations 


703 


2,480 


2,224 


6,076 


Significant at 1 percent (***), 
in parentheses. 


5 percent (**), a 


nd 10 percent (*) 


levels. Robust- V 


/bite t-statistics 



Regression estimates are from 1991-1995. Excess value is defined as the natural logarithm of the 
ratio of a firm's market-to-sales ratio to its imputed market-to-sales ratio. With the international 
benchmark, the firm's imputed performance is based on a weighted average of the imputed values 
for the various countries in which the firm operates. Firms with excess values that are greater 
than four or less than one-fourth are eliminated from the sample. The industry diversification 
dummy, SEG, is equal to one for firms who operate in more than one industry and zero otherwise. 
Multi-industry firms are defined as firms that operate in two or more two-digit SIC code 
industries and no firm segment sales exceed 90% of total firm sales. The multi-country 
diversification dummy, GEO, is equal to one for firms who operate in more than one country and 
zero otherwise. Multi-country firms are defined as firms that operate in two or more countries 
and no firm segment sales in a particular country exceed 90% of total firm sales. OIS is defined 
as the firm's operating income-to-sales, while CES is the firm's capital expenditures-to-sales. 



89 



For Japan, we omit the CES variable from the specification due to infrequently reported figures. 
Assets are defined as the natural logarithm of the firm's total assets. The independent variables 
OIS, CES, and ASSETS are all measured relative to the value of the weighted-average multiplier 
firms that form the basis for the excess value measure. Ownership concentration is defined as the 
sum of individual and/or institutional ownership holdings that are equal to or exceed five percent 
of a firm's common stock. OWNOtolO: = total ownership if total ownership < 0.10, = 0.10 if 
total ownership > 0.10; OWN10to30: = if total ownership < 0.10, = total ownership minus 
0.10 if 0.10 < total ownership < 0.30, = 0.20 if total ownership > 0.30; OWNover30: = if 
total ownership < 0.30, = total ownership minus 0.30 if total ownership > 0.30. Each model 
specification also includes year dummies for 1992-1995. 









90 



Table 3-5 
Multivariate Regression Estimates of Excess Values Using Only Pure-Play (SEG=0) Firms: 

1991 - 1995 



Panel A: Domestic Benchmark 



Variables 


German 
(1) 


Japanese 
(2) 


U.K. 
(3) 


U.S. 
(4) 


Constant 


-0.083 
(-1.02) 


0.194*** 
(3.74) 


-0.348*** 
(-5.85) 


-0 211*** 
(-7.35) 


Multi-Industry Segment 
Dummy (SEG) 


















Multi-Country Segment 
Dummy (GEO) 


-0.235** 
(-2.27) 


0.249*** 
(4.61) 


-0.024 
(-0.40) 


122*** 
(3.77) 


Relative Operating 
Income-to-Sales (OIS) 


0.036*** 
(3.73) 


0.070*** 
(2.65) 


0.226*** 
(5.82) 


0.018** 
(2.13) 


Relative Capital 

Expenditures-to-Sales 

(CES) 


0.042*** 
(3.45) 




0.057*** 
(6.42) 


0.083*** 
(7.04) 




Relative Total Assets 
(ASSETS) 


-0.128*** 
(-4.63) 


-0.053*** 
(-2.59) 


-0.021 
(-1.08) 


0.024* 
(1.88) 


Ownership 
Concentration < 1 
(OWNOtolO) 


3.504** 
(2.34) 


-1.326*** 
(-2.90) 


0.424 
(0.88) 


0.993*** 

(3.74) 


Ownership 
Concentration 10-30 
(OWN10to30) 


-1.328* 
(-1.73) 


0.314 
(1.37) 


-0.545** 
(-2.29) 


-0.624*** 
(-4.16) 


Ownership 
Concentration > 30 
(OWNover30) 


-0.131 
(-1.22) 


-0.735*** 
(-3.93) 


-0.257*** 
(-2.27) 


-0.078 
(-1.10) 


Ownership 
Concentration 10-30 
interacted with SEG 
(OWN10to30*SEG) 


















Ownership 
Concentration > 30 
interacted with SEG 
(OWNover30*SEG) 


















Ownership 
Concentration 10-30 
interacted with GEO 
(OWN10to30*GEO) 


1.229 
(1.59) 


-1.533** 
(-2.52) 


-0.273 
(-0.65) 


-0.627** 
(-2.21) 


Ownership 
Concentration > 30 
interacted with GEO 
(OWNover30*GEO) 


-0.281 
(-1.24) 


0.636 
(0.95) 


0.374* 
(1.74) 


0.340** 
(2.01) 


Adjusted R 2 


0.12 


0.09 


0.22 


0.07 


Number of Observations 


759 1,785 


2,269 


6,271 



91 





Panel B: International Benchmark 




Variables 


German 
(5) 


Japanese 
(6) 


U.K. 
(7) 


U.S. 

(8) 


Constant 


-0.069 
(-0.80) 


-0.091 
(-1.38) 


-0.384*** 
(-4.93) 


-0.207*** 
(-6.97) 


Multi-Industry Segment 
Dummy (SEG) 


















Multi-Country Segment 
Dummy (GEO) 


-0.106 
(-1.18) 


0.087 
(0.82) 


0.099 
(1.05) 


0.026 
(0.46) 


Relative Operating 
Income-to-Sales (OIS) 


0.016* 
(1.66) 


0.251*** 
(6.60) 


0.255*** 
(4.42) 


0.010 
(1.54) 


Relative Capital 

Expenditures-to-Sales 

(CES) 


0.030** 
(2.04) 




0.057*** 
(5.55) 


0.094*** 

(7.73) 




Relative Total Assets 
(ASSETS) 


-0.134*** 
(-4.31) 


-0.046*** 
(-2.81) 


0.002 
(0.09) 


0.030** 
(2.14) 


Ownership Concentration 
< lO(OWNOtolO) 


2.714* 
(1.73) 


-0.589 
(-1.07) 


0.456 
(0.91) 


0.821*** 
(2.83) 


Ownership Concentration 
10-30 (OWN10to30) 


-0.929 
(-1.25) 


-0.053 
(-0.22) 


-0.520** 
(-2.12) 


-0.588*** 
(-3.65) 


Ownership Concentration 
> 30 (OWNover30) 


-0.147 
(-1.39) 


-0.505*** 
(-2.84) 


-0.216* 
(-1.77) 


-0.041 

(-0.57) 


Ownership Concentration 
10-30 interacted with SEG 
(OWN10to30*SEG) 


















Ownership Concentration 
> 30 interacted with SEG 
(OWNover30*SEG) 


















Ownership Concentration 
10-30 interacted with GEO 
(OWN10to30*GEO) 


0.902 

(0.71) 


-0.767 
(-0.73) 


-1.411** 
(-2.12) 


-0.664 
(-1.29) 


Ownership Concentration 
> 30 interacted with GEO 
(OWNover30*GEO) 


-0.199 
(-0.46) 


0.495 
(0.55) 


0.080 
(0.24) 


0.393 
(1.32) 


Adjusted R 2 


0.10 


0.14 


0.25 


0.08 


Number of Observations 


517 


1,537 


1,684 


5,131 



Significant at 1 percent (***), 5 percent (**), and 10 percent (*) levels, Robust- W 
in parentheses. 



Regression estimates are from 1991-1995. Industrially diversified firms (SEG=1) are excluded 
from the sample. Excess value is defined as the natural logarithm of the ratio of a firm's market- 
to-sales ratio to its imputed market-to-sales ratio. With the domestic benchmark, the firm's 
imputed performance is based on a weighted average of the corresponding pure plays within the 
domestic market. The firm's imputed performance with the international benchmark is based on 
a weighted average of the imputed values for the various countries in which the firm operates. 
Firms with excess values that are greater than four or less than one-fourth are eliminated from the 
sample. The industry diversification dummy, SEG, is equal to one for firms who operate in more 
than one industry and zero otherwise. Multi-industry firms are defined as firms that operate in 
two or more two-digit SIC code industries and no firm segment sales exceed 90% of total firm 
sales. The multi-country diversification dummy, GEO, is equal to one for firms who operate in 
more than one country and zero otherwise. Multi-country firms are defined as firms that operate 



92 



in two or more countries and no firm segment sales in a particular country exceed 90% of total 
firm sales. OIS is defined as the firm's operating income-to-sales, while CES is the firm's capital 
expenditures-to-sales. For Japan, we omit the CES variable from the specification due to 
infrequently reported figures. Assets are defined as the natural logarithm of the firm's total 
assets. The independent variables OIS, CES, and ASSETS are all measured relative to the value 
of the weighted-average multiplier firms that form the basis for the excess value measure. 
Ownership concentration is defined as the sum of individual and/or institutional ownership 
holdings that are equal to or exceed five percent of a firm's common stock. OWNOtolO: = total 
ownership if total ownership < 0.10, = 0.10 if total ownership > 0.10; OWN10to30: = if total 
ownership < 0.10, = total ownership minus 0.10 if 0.10 < total ownership < 0.30, = 0.20 if total 
ownership > 0.30; OWNover30: = if total ownership < 0.30, = total ownership minus 0.30 if 
total ownership > 0.30. Each model specification also includes year dummies for 1992-1995. 



CHAPTER 4 
FIRM VALUE AND DERIVATIVE USAGE 

Introduction 

Several recent studies find that focused firms are generally more valuable than firms that 
are diversified along product lines. Berger and Ofek (1995), for instance, find that U.S. firms 
trade at discounts ranging from 13 to 15 percent during 1986-1991. Extending this evidence 
internationally, Lins and Servaes (1999) and Fauver, Houston and Naranjo (1999) also find that 
diversified firms in developed economies generally trade at valuation discounts relative to 
focused firms in those markets. While there are potential benefits to diversification, such as the 
ability to effectively use internal capital markets and other firm resources as well as potential tax 
benefits from leverage, the empirical evidence largely suggests that the costs of diversification 
generally outweigh these benefits. 1 Berger and Ofek (1995), Scharfstein and Stein (1997), Stein 
( 1 998), and Rajan, Servaes and Zingales ( 1 997) among other researchers show that the key costs 
of diversification arise from agency costs associated with intra-firm coordination problems that 
result in inefficient investment and cross-subsidization. 

The valuation effects associated with derivative usage by diversified firms also presents 
some potentially interesting insights into the magnitude of agency costs as a consequence of firm 
organizational form. That is, while the organizational structure of diversified firms provides 
some insights into the magnitude of potentially hedgeable risks that the firm may be exposed to, it 
also provides some additional insights into the magnitude of agency costs associated with 
derivative usage and corporate diversification. The agency costs associated with derivative usage 
are particularly relevant in light of several well-publicized cases of losses incurred by firms as a 



1 An exception to this evidence is Fauver, Houston, and Naranjo (1999) who find that the benefits 
from diversification outweigh the costs for firms in less-developed capital markets. 

93 



94 

result of their derivative trading practices. Because of these losses, there has been a significant 
increase in the attention paid to the risk management practices of corporations. 2 

The theoretical literature indicates that firm management uses derivative instruments for 
several reasons including tax motives, reduction in bankruptcy costs, and leverage, asymmetric 
information and moral hazard stories among others. Smith and Stulz (1985), for example, show 
that firms may hedge because of taxes and the transaction costs of financial distress. Hedging 
smoothes cash flows according to Froot, Scharfstein, and Stein (1993), which allows the firm to 
invest in projects when most needed. Myers (1977) and Stulz (1990) reasons that hedging may 
also diminish the investment distortions compared to debt financing. Stulz (1984) also argues 
that a manager's desire to reduce the volatility of their income may lead to managers hedging on 
behalf of the firm. Managers may also hedge to signal their own ability and expected payoff of a 
project to the market. Breeden and Viswanathan (1990), and Demarzo and Duffie (1995) propose 
this argument for derivative usage by firm managers. 

In terms of the empirical evidence, Tufano (1996) explores the gold industry and finds 
supporting evidence that managers hedge for risk aversion reasons. Haushlater (1997) utilizes the 
oil and gas industry to support the conclusions of Smith and Stulz (1984) in which firms hedge to 
reduce bankruptcy costs. Geczy, Minton, and Schrand (1997) determine that currency derivative 
usage and growth opportunities are positively correlated, which supports the arguments made by 
Froot, Scharfstein, and Stein (1993). Mayers and Smith (1982, 1987) provide hedging evidence 
from the insurance industry and show that insurance reduces bankruptcy costs, lowers debt 
contracting, decreases the expected tax burden, and transfers risk to the appropriate claimant. 



The availability of derivative instruments dates back to well over a century. In, 1851, for 
example, the Chicago Board of Trade (CBOT) recorded the first forward contract involving 3,000 
bushels of corn, while the first futures contract was recorded in 1865. However, it was not until 
1972 that the Chicago Board Options Exchange (CBOE) introduced the first financial future, and 
not until 1973 that the Chicago Mercantile Exchange (CME) started trading stock options. 
Today, over 280 million contracts are traded on the CBOT, and over two trillion dollars of futures 
and options are traded on the CME annually. 



95 

Mian (1987), however, finds that firms do not hedge to reduce possible financial distress. He 
finds mixed evidence regarding the use of financial instruments for debt contracting, taxes, and 
cash flow uncertainty. 

Relatively few studies have examined the direct relationship between the use of financial 
instruments and firm value. 3 Simkins (1998), Viswanathan (1998) and Aggarwal and Simkins 
(1999), for example, indicate that the use of financial instruments may lead to an increase in firm 
value. Allayannis and Weston (1998) also provide evidence from a sample of 720 nonfmancial 
firms from 1990 to 1995. They report that there is a positive relationship between currency 
derivative usage and Tobin's Q. Graham and Rogers (1999) also find that firm value is enhanced 
through the increase of debt capacity due to the use of financial instruments. These findings, 
however, contradict the conclusion suggested by Modigliani and Miller's (1958) seminal paper in 
which risk management strategies should be irrelevant to the value of the firm. 4 

In this paper, we further explore the relationship between firm value and derivative 
usage, with an emphasis on firms that are diversified along product lines. In the analysis, we 
gather data on over 1,600 firms headquartered in the U.S. during the 1991 through 1995 time 
period. We use a modification of the technique first adopted by Berger and Ofek (1995) to 
compute the implied value gain or loss from derivative usage on firm value. We find that focused 
firms that use derivative instruments have significantly higher unconditional average excess 
values than diversified firms that do not use them. After using regression procedures that control 
for firm characteristics including firm profitability, growth opportunities, size, leverage, and 
ownership concentration, We find that the value loss is greater for product diversified firms that 



The relative paucity of studies is partially due to the lack of detailed historical reporting of 
derivative instrument usage by firms. However, in response to the Financial Accounting 
Standards Board (FASB) requirements, the reporting of derivative instruments has improved 
considerably. 

4 According to Modigliani and Miller (1958), financing decisions are immaterial to the value of 
the firm ignoring taxes, transaction costs, and the cost of bankruptcy. 



96 

use derivatives, with the greatest value loss occurring for large diversified firms. These results 
are consistent with amplified agency costs in large, diversified firms. Using a Logit model to 
predict derivative usage, we also investigate how expected and unexpected derivative usage 
affects firm value. Interestingly, these results suggest that the value loss is associated with 
unexpected derivative usage by diversified firms. These findings also suggest that when firms 
use derivatives as expected, there are no valuation effects. 

The balance of this paper is as follows. The next section describes the data and 
methodology. The results are provided in the third section, while section four extends those 
results. Section five provides a conclusion. 

Data & Methodology 
Description of Data 

The Worldscope database is the principal data source used in this paper. The database 
discloses derivative information in the year-end financial statements. This information is mainly 
found in the footnotes to the firm's year-end statements. The derivative information is 
supplemented with 10-K reports from the SEC, which are available online through the EDGAR 
database. 5 The disclosure of derivative positions is mainly limited to the United States. 
Therefore, for my analysis, we concentrate on the 1991 through 1995 annual statements for firms 
in the United States. 

We use the footnote information to classify the types of derivatives used by each firm. 
Specifically, we use a separate dummy variable for currency, interest rate, and commodity 
derivatives. We include a dummy variable for derivative usage to encompass any one of the 
above types of derivatives. Similar to Lins and Servaes (1999) and Fauver, Houston, and Naranjo 
(1999), we assign firms into categories based upon their two-digit SIC code. These categories are 
defined at both the industry and geographic level (domestic versus multinational). We use the 
segment sales in the appropriate category of the firm to identify the type of firm. The firm must 



The website is www.sec.gov/edgarhp.htm. 



07 

have more than 10% of their total sales in each appropriate product segment and/or regional 
segment to be considered a diversified firm. The sample excludes firms whose primary business 
is financial services (i.e., SICs in the 6000-6999 range). These firms are excluded because sales 
figures are irregularly reported and are difficult to interpret for financial institutions. Finally, the 
sample excludes private firms because calculating market value of equity requires stock prices. 
Description of Methodology 

We use the "chop-shop" approach to assess the effects of derivative usage on excess 
value, where excess value is based on a modification of the technique first adopted by Berger and 
Ofek (1995). The ratio of total-capital-to-sales is used to measure firm value, where total capital 
is calculated by adding the market value of equity to the book value of debt. The difference 
between the firm's actual value and its imputed value is the measure of excess value. The actual 
value is determined by the consolidated firm's capital-to-sales ratio. The imputed value for 
single-segment domestic firms, not using derivatives, is measured as the median capital-to-sales 
ratio among all single-segment domestic firms, not using derivatives, within the same industry. 7 
The imputed value for multi-segment firms using derivatives is measured by forming a weighted- 
average of the imputed values for each of the firm's segments, where the weights reflect the 
proportion of the overall firm's sales that come from each segment. 

Multi-segment firms using derivatives with an actual value greater than their 
corresponding imputed value have a positive excess value (i.e., premium). Alternatively, multi- 
segment firms using derivatives with an actual value less than their corresponding imputed value 
have a negative excess value (i.e., discount). This occurs when a portfolio of pure-play firms, 
operating within the same industry, have a higher value relative to the multi-segment firms using 
derivatives. We group industries according to their two-digit SIC code level. The final sample 



6 Berger and Ofek (1995) consider two other ratios: the ratio of total capital (or pnce)-to-eamings 
and the ratio of total capital-to-assets. Their results are qualitatively similar for the three 
measures. 

I also obtain similar results when I stratify firms excluding the non-derivative usage restriction. 



98 

excludes excess values if the actual valuation is four times as large or one-fourth as large as the 
imputed valuation. This is common in previous studies and is used to eliminate nonsensical 
outliers from the sample. 

Results 
Unconditional Results 

Table 4-1 reports descriptive statistics for firms by both industrial diversification and 
derivative usage. If a firm uses currency, interest rate, or commodity derivatives, then it is 
classified as a derivative-using firm. The top number in each cell of Table 4-1 is the mean for 
each variable, while the bottom number in parentheses is the corresponding median value for 
each respective variable. The panel to the right of Table 4-1 reports statistical tests for 
differences in the mean and median value for each variable across the four firm types (focused 
non-derivative using firms (1), diversified non-derivative using firms (2), focused derivative 
using firms (3), and diversified derivative using firms (4)). 

There are some distinct differences that appear when comparing firms across the four 
categories in Table 4-1. The firms using derivatives are significantly larger as measured by total 
assets and total capital when compared to the firms not using derivatives. The order of magnitude 
is three to six times larger. Table 4-1 also shows that on every firm characteristic, excluding the 
market-to-sales ratio, firms using derivatives have significantly greater values than firms not 
using derivatives. In particular, firms using derivatives are more profitable as measured by the 
operating income-to-sales variable, have greater growth opportunities, and have higher leverage 
ratios. These findings are consistent with previous studies by Geczy, Minton, and Schrand (1997) 
and Graham and Rogers (1999). 

The levels of ownership concentration reported in Table 4-1 also vary across the four 
firm types, with the highest ownership concentration for focused firms not using derivatives and 
the lowest ownership concentration for diversified firms that use derivatives. Several recent 
studies suggest that firm value is correlated with ownership structure through variations in agency 



99 

costs associated with ownership concentration [e.g., Demsetz and Lehn (1985), Morck, Shleifer 
and Vishny (1988), Holderness and Sheehan (1998), and McConnell and Servaes (1990)] and that 
ownership structure vanes across countries and legal systems [e.g., La Porta, Lopez-de-Silanes, 
Shleifer and Vishny (1997, 1998), LaPorta, Lopez-De-Silanes and Shleifer (1999), and Claessens, 
Djankov, Fan and Lang (1998)]. Therefore, the low-level of ownership concentration among the 
diversified firms that use derivatives indicates that these firms face potentially higher agency 
costs and hence lower excess values. Consistent with this hypothesis, the mean and median 
excess values show that diversified firms who use derivatives experience the greatest value loss 
(approximately 9%). Interestingly, focused firms who use derivatives have the highest excess 
values. These results suggest that the effects of derivative usage on firm value potentially vary 
with the level of agency costs faced by the firm. Finally, the percentage of firms in the sample 
using derivatives is approximately 43%. This value is comparable to results reported in previous 
studies by Geczy, Minton, and Schrand (1997) and Allayannis and Weston (1998) who find that 
derivative usage by large firms ranges from 59 to 37 percent, respectively. 8 
Regression Results 

Although the results reported in Table 4-1 indicate that diversified firms that use 
derivatives have the lowest excess values whereas focused firms that use derivatives have the 
highest excess values, these results do not control for individual firm characteristics known to 
affect the firm's market-to-sales ratio. These characteristics constitute the size of the firm, 
profitability, future growth opportunities, leverage, and ownership concentration as discussed by 
Berger and Ofek (1995), Lins and Servaes (1999), and Fauver, Houston and Naranjo (1999). To 
more clearly disentangle the effects of derivative usage on firm value, it is necessary to control 



Allayannis and Weston (1999) examine firms with more than 500 million in total assets and 
only currency derivative usage. Geczy, Minton, and Schrand (1997) explore derivative usage of 
the largest Fortune 500 firms as measured by sales. When I examine the largest firms in my 
sample (by sales and assets), I obtain similar results to those of Geczy, Minton, and Schrand 
(1997). 



100 

for these firm characteristics. Based on the previous literature, we estimate the following 

regression model: 9 

(I) Excess Value = a + ^(Industrial Diversification Dummy) 

+$2{Geographic Diversification Dummy) + ^(Derivative Usage Dummy) 

+ ^(Derivative Usage interacted with Industrial Diversification) 

+ ^(Derivative Usage interacted with Geographic Diversification) 

+ ^(Relative Operating Income/Sales) + ^(Relative Capital Expenditures/Sales) 

+ $%(Log of Relative Assets) + ^(Relative Leverage) + ^(Ownership Concentration Levels) 

+ e. 

Firm excess value, the dependent variable, is defined as the natural log of the ratio of the 
firm's market value to its imputed value. For the independent variables, the industrial 
diversification dummy, SEGI, is set to one if the firm reports operating in more than one business 
segment and equal to zero otherwise. Similarly, the geographic diversification dummy, GSEGI, 
is set to one if the firm reports operating in more than one country and equal to zero otherwise. 
The derivative usage dummy, DERIVDUM, is assigned a value of one if the firm reports 
derivative usage and a value of zero otherwise. The relative operating income-to-sales variable, 
OIS, provides a measure of the firm's relative profitability, where as the relative capital 
expenditures-to-sales, CES, provides a measure of relative growth opportunities. Previous studies 
have indicated that there exists a positive relationship between relative capital expenditures-to- 
sales and relative operating income-to-sales on excess value. The log of relative assets, ASSETS, 
variable controls for potential size differences in the sample. Relative leverage, RELLEV, 
controls for possible capital structure differences that may explain differences in the market-to- 
sales ratios. Since the dependent variable is measured in relative terms, We also measure the 
independent variables in relative terms. In particular, the independent variables are all measured 



The reported conclusions are also robust to alternative specifications as well. 



101 

relative to the value of the weighted-average multiplier firms that form the basis for the excess 
value measure. 

Similar to Morck, Shleifer, and Vishny (1988) and others, we account for the nonlinear 
relation between ownership structure and its effect on firm value by creating three separate 
ownership concentration variables: 10 

OWNOtolO = total ownership if total ownership < 0.10, 
= 0.10 if total ownership > 0.10; 

OWN10to30 =0 if total ownership < 0.10, 

= total ownership minus 0.10 if 0. 10 < total ownership < 0.30, 
= 0.20 if total ownership > 0.30; 

OWNover30 =0 if total ownership < 0.30, 

■ total ownership minus 0.30 if total ownership > 0.30. 

The exact link between ownership structure and firm value is not entirely clear. On one 
hand, it is widely acknowledged that concentrated ownership is likely to reduce the conflicts that 
arise when there is a separation between managers and stockholders. This link suggests a positive 
relation between firm value and ownership concentration. On the other hand, concentrated 
ownership provides large investors with opportunities to exploit minority shareholders, thereby 
suggesting at least for some range of values a negative relation between firm value and ownership 
concentration. Finally, the regression specification also contains individual year dummies to 
allow for variations in economic conditions throughout the sample period. 

Table 4-2 summarizes the excess value regression results controlling for differences in 
firm charactenstics. The estimated coefficients on the industrial diversification dummy and the 



10 



Morck, Shleifer and Vishny (MSV, 1988) use 5 percent and 25 percent as their breakpoints. 
Given that the Worldscope databank does not generally provide firm level ownership 
concentration values below 5 percent (aside from the unreported values), I use a 10 percent cut- 
off for the first breakpoint and 30 percent as the next breakpoint to be consistent with MSV's 
ownership ranges. 



102 

geographic diversification dummy are consistent with earlier studies. In particular, looking at 
coefficients on SEGI and GSEGI, we find that the industrial diversification discount is 13.3% and 
the geographic diversification premium is 7.2%, both similar to earlier studies. These findings 
are consistent with the notion that, on average, product diversification is harmful to firm value 
and that geographic diversification is potentially beneficial. 

The results also examine the effect of derivative usage on excess value. The regression 
results indicate that derivative usage has no effect on firm excess value as shown by the 
insignificant coefficient of DERIVDUM. Consistent with implications made by Modigham and 
Miller's (1958), the insignificant effect of derivative usage on firm value indicates that derivative 
usage in and of itself neither enhances nor reduces firm value. Interestingly, when derivative 
usage is interacted with industrial diversification, there is a significant negative effect on firm 
value. This is consistent with the notion that for diversified firms that face potentially higher 
agency costs, derivative usage becomes a mechanism through which those costs are potentially 
enhanced. 

The regressions also reveal a significant and positive effect for relative operating-income- 
to-sales, capital expenditures-to-sales and assets on excess value. This is consistent with larger, 
more profitable firms having a higher valuation as measured by their market-to-sales ratios. The 
coefficient of relative leverage is negative and significant in the regression. This indicates that 
firms with higher relative leverage as measured by total debt divided by total assets of the firm 
are valued less. The estimated coefficients for ownership concentration levels indicate that, for 
levels between ten and thirty percent, there is a negative effect on firm excess value, suggesting 
that entrenchment problems and expropriation of minority shareholders is more of a concern for 
firms with relatively low-levels of ownership concentration. Finally, the coefficients of the 
annual dummies, not included in the table, are insignificantly different from zero, indicating little 
conditional intertemporal variation in excess values over the sample period. 



103 

Turning to Table 4-3, we separate firms into size quartiles as measured by total assets and 
investigate the relations among excess value, derivative usage, industrial diversification and 
geographic diversification. Similar to Table 4-2, the results indicate that derivative usage has no 
effect on firm value. However, for large firms, diversified firms that use derivatives, there is a 
significant negative effect on firm value. This suggests that the agency costs associated with 
inappropriate derivative usage are of particular concern for large, diversified firms that face 
severe agency problems. The coefficients on the control variables are similar to those reported 
earlier. The following section examines the expected and unexpected portions of derivative usage 
to further explore the potential sources of the negative effect of derivative usage for diversified 
firms. 
Results Separating Derivative Usage into Expected and Unexpected Components 

The results reported in the section above indicate that derivative usage in and of itself 
does not appear to have any valuation effects. That is, there appears to be both benefits and costs 
associated with derivative usage. However, for diversified firms that use derivatives, there 
appears to be a negative valuation effect, consistent with additional agency costs. To further get 
at the source of the negative valuation effect, in this section we examine expected and unexpected 
derivative usage on firm value. We hypothesize that unexpected derivative usage by firms should 
result in higher agency costs and hence lower firm value, whereas expected derivative usage 
should have no effect on firm value. To derive expected and unexpected derivative usage, we use 
a Logit regression model with derivative usage as the dependent variable and variables shown to 
affect derivative usage as independent variables (e.g., Geczy, Minton, and Schrand (1997)). The 
estimated Logit regression is as follows: 
(2) Derivative Usage, = a + ^{Industrial Diversification Dummy) t _ , 

+ ^{Geographic Diversification Dummy) t ^ + ^{Industrial and Geographic interaction)^ , 
+ ^{Operating Income-to-Sales\. , + ^{Capital Expenditure-to-Sales\. , 
+ MLog of Total Assets),. , + ^{Leverage),. , + ^{Ownership Levels),. , + e,. 



104 



Equation (2) is used to separate derivative usage into its expected and unexpected 
components. As with the earlier analysis, we use the footnote information to classify firms into 
derivative and non-derivative using firms. This binary indicator variable is used as the dependant 
variable in the Logit regression shown above. Similar to earlier studies, the independent variables 
are in levels and are for time t-1. The reason is that the expected and unexpected derivative usage 
of the firm at time t will be evaluated at time t-1, considering all relevant information is known at 
this time. The expected derivative usage portion is calculated from the predicted value from the 
Logit regression, and the unexpected derivative usage portion is the residual." These results are 
summarized in Table 4-4. The results indicate that industrially diversified firms are more likely 
to use derivative instruments. Firms with greater growth opportunities, as measured by capital 
expenditures-to sales, are also more likely to use derivatives. Larger firms, as measured by total 
assets, are more likely the firm is to utilize derivatives. Higher levered firms are less likely to use 
derivatives. There is no effect on derivative usage for firms paying a dividend. Ownership levels 
appear to yield mixed results. Firms with lower concentrated ownership levels (defined below 
10%) indicate a positive relationship for derivative usage, while firms with higher concentrated 
ownership levels (defined above 30%) are less likely to use derivatives. 

The expected and unexpected values obtained from Table 4-4 regressions are now 
incorporated into equation (1). These results are shown in Table 4-5. The reported coefficient 
estimates are largely similar to those reported in Table 4-2. Interestingly, however, unexpected 
derivative usage by diversified firms has a significantly negative effect on firm value. This 
suggests that the negative valuation effects of derivative usage for diversified firms reported 
earlier is the result of unexpected derivative usage by these firms. Expected derivative usage, on 
the other hand, has no valuation effects for these firms and the other firms in the sample. This 
indicates that derivative usage, when expected, neither enhances nor reduces firm value. It is the 



" I also calculated the equation using a Probit regression model. The correlations between the 
Logit and Probit models were almost one. 






105 

unexpected part of derivative usage by diversified firms, who likely face severe agency costs as a 
consequence of their organizational form, that are negatively effected. 

Conclusion 
In this paper, we investigate the effects of derivative usage on firm excess value as well 
as the interactions among derivative usage, product diversification, geographic diversification, 
and firm excess value. In the analysis, we gather data on over 1,600 firms headquartered in the 
U.S. during the 1991 through 1995 time period. We use a modification of the technique first 
adopted by Berger and Ofek (1995) to compute the implied value gain or loss from derivative 
usage on firm value. We find that focused firms that use derivative instruments have significantly 
higher unconditional average excess values than diversified firms that do not use them. After 
using regression procedures that control for firm characteristics including firm profitability, 
growth opportunities, size, leverage, and ownership concentration, we also find that the value loss 
is greater for product diversified firms that use derivatives, with the greatest value loss occurring 
for large diversified firms. Interestingly, results that differentiate between expected and 
unexpected derivative usage and control for individual firm characteristics suggest that the value 
loss is associated with unexpected derivative usage by diversified firms. 
| Overall, the findings in this paper suggest that diversified firms that potentially face 

higher agency costs are more likely to be negatively affected by derivative usage, particularly if 
they unexpectedly use them. On the other hand, the findings m this paper also suggest that 
derivative usage is not necessarily value enhancing nor value destroying. It is the interaction 
between firms that face potential agency costs and derivative usage that are of particular concern. 
In sum, it appears that there are both benefits and costs associated with derivative usage. To 
mitigate the costs, it is important that derivative usage by firms be closely monitored. 



106 



Table 4-1 
Summary Statistics for U.S. Firms by Industrial Diversification (SEGI) and Derivative Usage: 

1991 - 1995 



Firm Level Characteristics by 
Derivative Usage 


Firms Not Using Derivatives 


Firms Using Derivatives 


SEGI = 
(1) 


SEGI = 1 

(2) 


SEGI = 
(3) 


SEGI = 1 

(4) 


Total Assets (mil $) 


795 
(176) 


645 
(283) 


2,810 
(730) 


4,210 
(1,580) 


Total Capital (mil $) 


538 
(120) 


436 
(191) 


1,710 
(484) 


2,480 
(979) 


Leverage Ratio 


0.259 
(0.240) 


0.264 
(0.250) 


0.274 
(0.252) 


0.289 
(0.276) 


Operating Income/Sales 


0.117 
(0.122) 


0.113 
(0.114) 


0.177 
(0.147) 


0.151 
(0.135) 


Capital Expenditure/Sales 


0.085 
(0.048) 


0.074 
(0.042) 


0.114 
(0.059) 


0.088 
(0.053) 


Research & Development 
(mil $) 


6.0 
(0.4) 


6.9 
(0.6) 


87.9 
(11.4) 


156 
(23.8) 


Market/Sales 


1.849 
(1.160) 


1.322 
(0.960) 


1.701 
(1.245) 


1.351 
(1.014) 


Ownership Concentration 


0.296 
(0.264) 


0.268 
(0.234) 


0.252 
(0.205) 


0.197 
(0.143) 


Excess Value 


0.023 
(0.001) 


-0.089 
(-0.082) 


0.097 
(0.107) 


-0.088 
(-0.091) 


Observations 


3,012 


847 


1,849 


1,012 



107 





Table 4-1— 


continued 








Firm Level Characteristics 
by Derivative Usage 


Test of Statistical Differences p- values 


(l)-(2) 


(l)-(3) 


(l)-(4) 


(2) -(3) 


(2) -(4) 


(3) -(4) 


Total Assets (mil S) 


0.007 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


Total Capital (mil $) 


0.007 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


Leverage Ratio 


0.503 
(0.127) 


0.013 
(0.002) 


0.000 
(0.000) 


0.250 

(0.483) 


0.005 
(0.000) 


0.035 
(0.000) 


Operating Income/Sales 


0.698 
(0.035) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.001) 


Capital Expenditure/Sales 


0.009 
(0.359) 


0.000 
(0.000) 


0.503 
(0.000) 


0.000 
(0.000) 


0.007 
(0.000) 


0.000 
(0.027) 


Research & Development 
(mil $) 


0.204 
(0.112) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


Market/Sales 


0.000 
(0.000) 


0.023 
(0.002) 


0.000 
(0.002) 


0.000 
(0.000) 


0.618 
(0.006) 


0.000 
(0.000) 


Ownership Concentration 


0.003 
(0.002) 


0.000 
(0.000) 


0.000 
(0.000) 


0.096 
(0.031) 


0.000 
(0.000) 


0.000 
(0.000) 


Excess Value 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.000 
(0.000) 


0.956 

(0.864) 


0.000 
(0.000) 


Observations 















Derivative usage includes currency, interest rate, and commodity derivatives. The upper number 
in each cell reports the mean value for each variable, while the lower number in parentheses 
reports the median value for each variable. T- tests are used to test for differences in each 
respective mean value, while a Wilcoxon rank-sum test is used to test for differences in the 
median values. The leverage ratio is defined as book value of debt divided by total assets. Due 
to missing R&D data, the observations are slightly less than for the other variables. Market-to- 
sales is defined as the ratio of a firm's market value of equity plus book value of debt to its total 
sales. 



Table 4-2 

Multivariate Regression Estimates of Excess Values for Derivative Usage Controlling for Firm 

Characteristics: 1991-1995 



Variables 


Excess Value 
Regression 


Constant 


0.015 
(0.574) 


Multi-Industry Segment Dummy 
(SEGI) 


-0.133*** 
(-6.162) 


Multi-Country Segment Dummy 
(GSEGI) 


0.072** 
(2.497) 


Derivative Dummy (DERIVDUM) 


0.007 

(0.347) 


Derivative Dummy interacted with 
SEGI (SDERIV) 


-0.072** 
(-2.323) 


Derivative Dummy interacted with 
GSEGI (GDERIV) 


-0.062 
(-1.374) 


Relative Operating Income-to-Sales 
(OIS) 


0.006*** 
(2.631) 


Relative Capital Expenditures-to- 
Sales (CES) 


0.047*** 
(6.128) 


Relative Total Assets (ASSETS) 


0.083*** 
(6.765) 


Relative Leverage (RELLEV) 


-0.041*** 
(-6.269) 


Ownership Concentration < 10 
(OWNOtolO) 


0.213 
(0.868) 


Ownership Concentration 10-30 
(OWN10to30) 


-0.316** 
(-2.332) 


Ownership Concentration > 30 
(OWNover30) 


0.060 
(0.929) 


Adjusted R" 


0.066 


Number of Observations 


6,720 



Significant at 1 percent (***), 5 percent (**), and 10 percent (*) levels; Robust- White t-statistics 
in parentheses. 

Regression estimates are from 1991-1995. Excess value is defined as the natural logarithm of the 
ratio of a firm's market-to-sales ratio to its imputed market-to-sales ratio. Firms with excess 
values that are greater than four or less than one-fourth are eliminated from the sample. The 
industry diversification dummy, SEGI, is equal to one for firms who operate in more than one 
industry and zero otherwise. Multi-industry firms are defined as firms that operate in two or more 
two-digit SIC code industries and no firm segment sales exceed 90% of total firm sales. The 
multi-country diversification dummy, GSEGI, is equal to one for firms who operate in more than 
one country and zero otherwise. Multi-country firms are defined as firms that operate in two or 
more countries and no firm segment sales in a particular country exceed 90% of total firm sales. 
Derivative usage includes currency, interest rate, and commodity derivatives. OIS is defined as 
the firm's operating income-to-sales, while CES is the firm's capital expenditures-to-sales. Assets 



109 



are defined as the natural logarithm of the firm's total assets. The leverage ratio is defined as 
book value of debt divided by total assets. OIS, CES, and ASSETS are all measured relative to 
the value of the weighted-average multiplier firms that form the basis for the excess value 
measure. Ownership concentration is defined as the sum of individual and/or institutional 
ownership holdings that are equal to or exceed five percent of a firm's common stock. 
OWNOtolO: = total ownership if total ownership < 0.10, = 0.10 if total ownership > 0.10; 
OWN10to30: = if total ownership < 0.10, = total ownership minus 0.J0 if 0.10 < total 
ownership < 0.30, = 0.20 if total ownership > 0.30; OWNover30: = if total ownership < 0.30, 
= total ownership minus 0.30 if total ownership > 0.30. The model specification also includes 
year dummies for 1992-1995. 






110 



Table 4-3 
Multivariate Regression Estimates of Excess Values for Derivative Usage by Firm Size: 

1991 - 1995 



Variables 


Asset Size Quartile 


Small 


(2) 


(3) 


Large 


Constant 


-0.012 
(-0.195) 


-0.079* 
(-1.488) 


-0.024 
(-0.488) 


0.222*** 
(4.747) 


Multi-Industry Segment Dummy 
(SEGI) 


-0.174*** 
(-4.145) 


-0.152*** 

(-3.838) 


-0.110*** 
(-2.638) 


-0.043 
(-0.901) 


Multi-Country Segment Dummy 
(GSEGI) 


0.060 
(1.229) 


0.101* 
(1.857) 


0.144*** 
(2.711) 


-0.138** 
(-2.182) 


Derivative Dummy 
(DERIVDUM) 


0.018 
(0.353) 


-0.040 
(-0.989) 


0.045 

(1.222) 


-0.001 
(-0.040) 


Derivative Dummy interacted 
with SEGI (SDERIV) 


-0.138 
(-1.610) 


-0.068 
(-0.898) 


-0.086 
(-1.466) 


-0.126** 
(-2.195) 


Derivative Dummy interacted 
with GSEGI (GDERIV) 


-0.216* 

(-1.877) 


-0.005 
(-0.046) 


-0.091 
(-1.205) 


0.107 
(1.281) 


Relative Operating Income-to- 
Sales (OIS) 


0.023** 
(2.419) 


0.006 
(1.328) 


0.010 
(0.951) 


0.003* 
(1.719) 


Relative Capital Expenditures-to- 
Sales (CES) 


0.070*** 
(4.501) 


0.052*** 
(3.299) 


0.054*** 
(4.011) 


0.027*** 
(3.198) 


Relative Total Assets (ASSETS) 


0.040 
(0.927) 


0.111*** 
(2.673) 


0.082** 
(2.296) 


-0.027 
(-1.032) 


Relative Leverage (RELLEV) 


-0.051*** 

(-4.753) 


-0.018* 

(-1.772) 


-0.030** 
(-2.400) 


-0.061*** 
(-5.846) 


Ownership Concentration < 1 
(OWNOtolO) 


1.252* 
(1.893) 


0.426 
(0.716) 


0.404 
(0.823) 


-0.721* 
(-1.794) 


Ownership Concentration 10-30 
(OWN10to30) 


-1.176*** 
(-4.080) 


-0.257 
(-0.959) 


-0.110 
(-0.435) 


0.392 

(1.372) 


Ownership Concentration > 30 
(OWNover30) 


0.149 
(1.133) 


0.043 
(0.345) 


-0.118 
(-1.057) 


0.241* 
(1.663) 


Adjusted R 1 


0.106 


0.066 


0.070 


0.064 


Number of Observations 


1,680 


1,680 


1,680 


1,680 


Significant at 1 percent (***), 5 pe rc< 


mt(**), and 1C 


percent (*) le\ 


'els; Robust-W 


lite t-statistics 



in parentheses. 



Regression estimates are from 1991-1995. The small quartile category comprises the smallest 
firms as measured by log of total assets, whereas the large category comprises the largest firms. 
Excess value is defined as the natural logarithm of the ratio of a firm's market-to-sales ratio to its 
imputed market-to-sales ratio. Firms with excess values that are greater than four or less than 
one-fourth are eliminated from the sample. The industry diversification dummy, SEGI, is equal 
to one for firms who operate in more than one industry and zero otherwise. Multi-industry firms 
are defined as firms that operate in two or more two-digit SIC code industries and no firm 
segment sales exceed 90% of total firm sales. The multi-country diversification dummy, GSEGI, 
is equal to one for firms who operate in more than one country and zero otherwise. Multi-country 
firms are defined as firms that operate in two or more countries and no firm segment sales in a 
particular country exceed 90% of total firm sales. Derivative usage includes currency, interest 
rate, and commodity derivatives. OIS is defined as the firm's operating income-to-sales, while 



Ill 



CES is the firm's capital expenditures-to-sales. Assets are defined as the natural logarithm of the 
firm's total assets. The leverage ratio is defined as book value of debt divided by total assets. 
OIS, CES, and ASSETS are all measured relative to the value of the weighted-average multiplier 
firms that form the basis for the excess value measure. Ownership concentration is defined as the 
sum of individual and/or institutional ownership holdings that are equal to or exceed five percent 
of a firm's common stock. OWNOtolO: = total ownership if total ownership < 0.10, = 0.10 if 
total ownership > 0.10; OWN10to30: = if total ownership < 0.10, = total ownership minus 
0.10 if 0.10 < total ownership < 0.30, = 0.20 if total ownership > 0.30; OWNover30: = if 
total ownership < 0.30, = total ownership minus 0.30 if total ownership > 0.30. The model 
specification also includes year dummies for 1992-1995. 






112 



Table 4-4 
Logit Regression Estimates of Derivative Usage: 1992 - 1995 



Variables 


Derivative Usage 
Dummy 


Constant 


-13.226*** 
(-26.366) 


Multi-Industry Segment Dummy 
(SEGI) 


0.362*** 
(4.814) 


Multi-Country Segment Dummy 
(GSEGI) 


0.046 
(0.373) 


Industry Segment Dummy interacted 
with Country dummy (SEGIGSEGI) 


0.021 
(0.098) 


Operating Income-to-Sales (OIS) 


0.225 
(1.602) 


Capital Expenditures-to-Sales (CES) 


0.382 
(1.137) 


Log Total Assets (ASSETS) 


1.469*** 
(25.363) 


Leverage (LEV) 


-0.335** 
(-2.102) 


Dividend Dummy (DIVDUM) 


-0.010 
(-0.135) 


Ownership Concentration < 10 
(OWNOtolO) 


2.633** 
(2.272) 


Ownership Concentration 10-30 
(OWN10to30) 


0.251 
(0.410) 


Ownership Concentration > 30 
(OWNover30) 


-0.480* 
(-1.783) 


Pseudo R^ 


0.166 


Number of Observations 


5,186 



Significant at 1 percent (***), 5 percent (**), and 10 percent (*) levels; Robust- White t-statistics 
in parentheses. 

Estimates based on the following Logit regression equation: 

Derivative Usage, = a + & (Industrial Diversification Dummy), _ , 

+ [^(Geographic Diversification Dummy),. , + p 3 (Industrial and Geographic interaction),. , 

+ p 4 (Operating Income-to-Sales), _ , + p 5 (Capital Expenditure-to-Sales), , 

+ P 6 (Log of Total Assets),. , + Pv(Leverage), , + p 8 (Dividend Dummy), _ , 

+ P9(Ownership Concentration Levels), . , + e,. 

Logit regression estimates are from 1991-1995. Derivative usage includes currency, interest rate, 
and commodity derivatives. The industry diversification dummy, SEGI, is equal to one for firms 
who operate in more than one industry and zero otherwise. Multi-industry firms are defined as 
firms that operate in two or more two-digit SIC code industries and no firm segment sales exceed 
90% of total firm sales. The multi-country diversification dummy, GSEGI, is equal to one for 
firms who operate in more than one country and zero otherwise. Multi-country firms are defined 
as firms that operate in two or more countries and no firm segment sales in a particular country 
exceed 90% of total firm sales. OIS is defined as the firm's operating income-to-sales, while 



113 



CES is the firm's capital expenditures-to-sales. Assets are defined as the natural logarithm of the 
firm's total assets. The leverage ratio is defined as book value of debt divided by total assets. 
Dividend dummy is equal to one if the firm paid a dividend and zero otherwise. Ownership 
concentration is defined as the sum of individual and/or institutional ownership holdings that are 
equal to or exceed five percent of a firm's common stock. OWNOtolO: = total ownership if total 
ownership < 0.10, = 0.10 if total ownership > 0.10; OWN10to30: = if total ownership < 0.10, 
= total ownership minus 0.10 if 0.10 < total ownership < 0.30, = 0.20 if total ownership > 0.30; 
OWNover30: = if total ownership < 0.30, = total ownership minus 0.30 if total ownership > 
0.30. The model specification also includes year dummies for 1993-1995. 



114 



Table 4-5 

Multivariate Regression Estimates of Excess Values for Expected and Unexpected Derivative 

Usage Controlling for Firm Characteristics: 1992 - 1995 



Variables 


Excess Value 
Regression 


Constant 


-0.008 
(-0.198) 


Multi-Industry Segment Dummy 
(SEGI) 


-0.159*** 
(-3.547) 


Multi-Country Segment Dummy 
(GSEGI) 


0.008 
(0.146) 


Expected Derivative Usage 


-0.040 
(-0.565) 


Unexpected Derivative Usage 


0.004 
(0.381) 


Expected Derivative Usage 
Dummy interacted with SEGI 
(SDERVEXP) 


-0.002 
(-0.027) 


Unexpected Derivative Usage 
Dummy interacted with SEGI 
(SDERVUNEX) 


-0.044** 
(-2.559) 


Expected Derivative Usage 
Dummy interacted with GSEGI 
(GDERVEXP) 


0.067 

(0.557) 


Unexpected Derivative Usage 
Dummy interacted with GSEGI 
(GDERVUNEX) 


-0.023 
(-0.917) 


Relative Operating Income-to- 
Sales (OIS) 


0.005*** 
(2.596) 


Relative Capital Expenditures- 
to-Sales (CES) 


0.047*** 
(5.422) 


Relative Total Assets (ASSETS) 


0.099*** 
(4.593) 


Relative Leverage (RELLEV) 


-0.034*** 
(-4.585) 


Ownership Concentration < 1 
(OWNOtolO) 


0.164 
(0.588) 


Ownership Concentration 10-30 
(OWN10to30) 


-0.365** 
(-2.443) 


Ownership Concentration > 30 
(OWNover30) 


0.058 
(0.787) 


Adjusted R^ 


0.067 


Number of Observations 


5,186 



Significant at 1 percent (***), 5 percent (**), and 10 percent (*) levels, Robust- White t-statistics 
in parentheses. 



Excess value is defined as the natural logarithm of the ratio of a firm's market-to-sales ratio to its 
imputed market-to-sales ratio. Firms with excess values that are greater than four or less than 
one-fourth are eliminated from the sample. The industry diversification dummy, SEGI, is equal 



115 



to one for firms who operate in more than one industry and zero otherwise. Multi-industry firms 
are defined as firms that operate in two or more two-digit SIC code industries and no firm 
segment sales exceed 90% of total firm sales. The multi-country diversification dummy, GSEGI, 
is equal to one for firms who operate in more than one country and zero otherwise. Multi-country 
firms are defined as firms that operate in two or more countries and no firm segment sales in a 
particular country exceed 90% of total firm sales. Expected and unexpected derivative usage are 
based on the estimates from Table 4-4. OIS is defined as the firm's operating income-to-sales, 
while CES is the firm's capital expenditures-to-sales. Assets are defined as the natural logarithm 
of the firm's total assets. The leverage ratio is defined as book value of debt divided by total 
assets. OIS, CES, and ASSETS are all measured relative to the value of the weighted-average 
multiplier firms that form the basis for the excess value measure. Ownership concentration is 
defined as the sum of individual and/or institutional ownership holdings that are equal to or 
exceed five percent of a firm's common stock. OWNOtolO: = total ownership if total ownership 
< 0.10, = 0.10 if total ownership > 0.10; OWN10to30: = if total ownership < 0.10, = total 
ownership minus 0.10 if 0.10 < total ownership < 0.30, = 0.20 if total ownership > 0.30; 
OWNover30: = if total ownership < 0.30, = total ownership minus 0.30 if total ownership > 
0.30. The regression specification also includes year dummies for 1993-1995. 






CHAPTER 5 
DISCUSSION AND CONCLUSION 

We first find that the negative value associated with product diversification in the U.S., 
does not necessarily hold in less developed countries. Specifically, we conclude that there is an 
inverse relationship between the value of product diversification and the level of capital market 
development. We also determine that the value of product diversification varies with the legal 
system in the country where the firm is headquartered. These results indicate that the 
environment in which the firm operates plays a part on the value of the firm. 

Secondly, we determine the effect of product and geographic diversification on firm 
value. We find that on average, geographic diversification has no effect on firm value in 
Germany and the United Kingdom. There is, however, a benefit to geographic diversification in 
Japan and the United States. This leads one to believe that firms operating within these countries 
are generating greater operating efficiencies or greater risk reduction. However, firms in these 
four countries achieve no greater benefit than an international portfolio of domestic firms 
operating in their line of business. 

Finally, unconditional derivative usage by focused firms has a positive effect on firm 
excess value, and a negative effect on industrially diversified firms. Results controlling for firm 
characteristics indicate that derivative usage by industrially diversified firms has a negative effect 
on firm value. The negative effect is greater for larger, diversified firms. The results 
differentiating between expected and unexpected derivative usage indicate that unexpected 
derivative usage by industrially diversified firms has a negative effect on firm value, while, 
expected derivative usage in itself has no valuation effect on firm value. These findings are 
consistent with greater agency costs associated with product diversified firms. Therefore, 
managers and shareholders should carefully monitor potential agency costs associated with 
diversified firms. 

116 









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BIOGRAPHICAL SKETCH 
Larry A. Fauver was born in Youngstown, Ohio in 1969. He received his B.E. in 
mechanical engineering at Youngstown State University in 1992. He then received an MBA in 
general business at Youngstown State University m 1994. In 1995, he started his Ph.D. at the 
University of Florida. In 1997, he received an MA. in economics. He will graduate in August 
2000 and continue his career in the finance department at the University of Miami as an assistant 
professor. 



123 



I certify that I have read this study and that in my opinion it conforms to acceptable 
standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for 
the degree of Doctor of Philosophy. 




Ui d^A 



Joel/F. Houston, Chair 
Associate Professor of Finance, Insurance, and 
Real Estate 



I certify that I have read this study and that in my opinion it conforms to acceptable 
standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for 
the degree of Doctor of Philosophy. 



Andy Naranjo, Cfochair X 

Associate Professor of Finance, insurance, and 
Real Estate 



I certify that I have read this study and that in my opinion it conforms to acceptable 
standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for 
the degree of Doctor of Philosophy. 




Mark^l. fllannery 
Barnettoank Eminent ScFk 
Insurance, and Real Estate 



Finance, 



I certify that I have read this study and that in my opinion it conforms to acceptable 
standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for 
the degree of Doctor of Philosophy. 



0~&V-| 





Peter J. van Blokland 

Professor of Food and Resource Economics 



This dissertation was submitted to the Graduate Faculty of the Department of Finance, 
Insurance, and Real Estate in the Warrington College of Business Administration and to the Graduate 
School and was accepted as partial fulfillment of the requirements for the degree of Doctor of 
Philosophy. 

August 2000 



Dean, Graduate School 







UNIVERSITY OF FLORIDA 



3 1262 08555 0266 



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